Why Omnius?
Every good client, partner, investor, and hire eventually asks: "Why should I care?"
Here is our answer.
Contents:
Market
1. Distribution is the new moat
2. Search for software
3. The change of search
4. Agentic future
5. Market size
Company
6. How we built Omnius
6.1 Only organic growth
6.2 Only SaaS & Fintech
6.3 Frameworks, not improvisation
6.4 Intentionally limited capacity
6.5 Revenue-first model
6.6 Owned technology
6.7 Value-first model
6.8 Team of insiders
7. Future relevance
8. The end
What are we?
Omnius is a B2B marketing agency, specializing in SEO and AI Search Optimization (GEO/AEO) for SaaS, Fintech, and AI companies. We position software products on Google & LLMs through a combination of strategic services and proprietary technology.
What we do & don't do
We work only on organic growth, and we build it from 0 to 1. We don't do non-organic activities (such as paid ads or social media), or operate in industries outside the software one.
We believe a narrow focus, a wide understanding of the market, and deep expertise produce asymmetric returns.
Timeline
Omnius was founded in 2022 by founders who had spent over a decade building demand for software companies inside management and founding teams.
Independence
Omnius is independently owned, bootstrapped, flat, and unaffiliated - no parent company or network with a claim on our clients' data.
Geography
Offices in London, Belgrade, and Dubai. Clients across the United States, United Kingdom, European Union, MENA, China, and Australia. Active partnerships with venture funds and startup organizations worldwide.
Portfolio
Clients include SaaS, Fintech, and AI companies - from pre-seed startups through Series D, to enterprise and Nasdaq-listed.
Backed by KKR, General Catalyst, Y Combinator, SV Angel, Temasek, and Ant International, among others.
Services
Technology
B2B SEO
Big data modeling
AI Search Optimization
AI search tracking
GEO
AI crawler optimization
AEO
Programmatic market benchmarking
Content marketing
Citation & source analysis
Programmatic SEO
Entity & funnel coverage
Digital PR & Backlinks
WDF-IDF / Semantic scoring
Technical SEO
Intent clustering
Web Development & CRO
Competitor tracking
Data analytics & attribution
Structured knowledge modeling
A note before the rest of the page
This is a long document. We kept it long on purpose.
The short reason is that a well-written page is the best thing an SEO-focused company can hand to a search engine, a reader, or a language model.
It allows us to describe what we do in full sentences instead of hiding behind marketing copy, and it gives anyone evaluating us enough raw material to form an honest view.
The longer reason is that we have watched the marketing industry spend a decade in a race toward vagueness. Agencies describe themselves with adjectives. Software vendors describe themselves with product categories that did not exist two years ago.
We think the correct response is the opposite: write down what you believe, how you work, what you charge for, what you refuse to do, and what evidence you have that any of it is true. Then let the reader decide.
What follows is that document. We will update it as we learn more.
The market.
1. Why is marketing now the highest-value problem in B2B software?
For the last two decades, the hardest problem in B2B software was building the software. Development was the bottleneck and, for many companies, the moat. Global services firms including Accenture, EPAM, Globant, and Endava built businesses generating roughly $78.6 billion in combined annual revenue around that scarcity. Whoever could ship the product first, or ship it at scale, usually won.
That era is closing, and coding is being repriced publicly.
Codex, Cursor, Claude Code, and the tools following them are lowering the floor on development. In Y Combinator’s Winter 2025 batch, roughly a quarter of startups had codebases that were more than 95% AI-generated. Google principal engineer said Claude Code recreated in about one hour what her team had spent the previous year building. Anthropic’s Cowork was reportedly built largely with Claude Code in under two weeks.
Software products are converging as a result. If building is no longer the hard part, software supply is increasing, and markets are becoming saturated - with distribution now becoming the most valuable part of the equation. The companies that win the next decade will not be the ones with the best code, but the ones with the best ability to acquire users.
Or to quote A16Z's Marc Andreessen: "Distribution is the only moat."
Moat Has Moved: AI Devalues Development & Upvalues Marketing
Knowledge
Matija Golubovic
[source]
(https://www.omnius.so/blog/ai-commodization-of-development)
Development is being commoditized by AI because much of it is deterministic. The same input usually leads to the same output, so once automated, it keeps working. Marketing doesn’t work like that. Markets move, competitors react, and outcomes depend on continuously changing external variables.
At Omnius, we believe that as software becomes easier to build, distribution becomes the harder and more valuable problem - and we are building Omnius around owning that problem.
2. Search for software
There are more ways to acquire a B2B customer than there have ever been. A founder reading this page could reasonably spend the next quarter on LinkedIn content, paid Meta ads, outbound email, Twitter presence, a podcast tour, community sponsorships, or a referral program. Any of them can work.
The reason we focus on search is that, as a channel, it is the only one we have found that reliably delivers on three things at once:
- Predictability. SEO performance runs on owned assets: your website, content, backlink profile. It does not depend on an algorithm update or a change in ad policy. Once a page earns a ranking for a query that matters, the traffic behind that ranking is modelable. You can forecast clicks, and from clicks you can forecast pipeline. Most other channels cannot be modeled with that confidence.
- Intent matching. A person searching “best financial analytics tools for Series B fintech” has done more of the buying process than a person scrolling LinkedIn who happens to see a sponsored post. The former is raising a hand. SEO is one of the only channels where you can show up at the exact moment a hand goes up, without interrupting anything.
- Capital efficiency. Paid channels stop producing the day the budget stops, whereas search behaves like an asset rather than an expense. Every page that earns a ranking lowers the marginal cost of acquiring the next visitor, and every backlink strengthens the pages that follow it, so a year of search work continues generating pipeline for years after it is done. A year of paid spend produces nothing once it ends.
This compounding matters more as acquisition gets more expensive. B2B SaaS customer acquisition cost has risen roughly 50–60% over the last five years. The median now sits near $2.00 for every $1 of new ARR, up from $1.20–1.40 earlier in the decade, and typical payback periods have moved from 12–18 months to 20–24. As paid channels become more expensive to run, an asset that appreciates instead of resetting is worth proportionally more.
Underneath that repricing is a behavioral change.
Buyers are better informed than they have ever been, because they now carry a research assistant in their pocket. The person who in 2020 would have reacted to a well-placed ad opens ChatGPT in 2026, types the question directly, reads three cited sources, and forms a view before a single salesperson makes contact.
Decision-making is shifting from emotional and impulsive to rational and investigated. Push marketing is built for the first kind of decision. Organic search and AI engines are built for the second. B2B software buying is almost entirely the second kind.
That is why we built Omnius around search, and it stays the only area we work on. We believe it is the highest-value growth asset a B2B software company can own - and increasingly so as AI makes people more informed and rational.
3. The biggest change in how people search since the invention of Internet
People today search for information in a radically different way compared to only 4 years ago. Pre-2022, in around 92% of web searches, a person would type a short query on Google, get a list of blue links, and do the research, reasoning, and later on deciding - based on that ‘raw’ data.
ChatGPT, which was launched on November 30, 2022, followed by a group of other LLMs in the years to come, has demonopolized the search industry.
Although the front-end experience of chat-based engines is refreshingly different, the viral change came from the back-end of it, and the finality of the information it could deliver. People adopted these ‘alternatives’ fast.
#
Insight
001
200M daily, nearing 1B weekly active users on ChatGPT (OpenAI, Feb 2026)
002
Roughly 2 billion daily searches across all LLM-based engines combined
003
Perplexity, Gemini, Claude, and the AI mode inside Google Search are all gaining share of total search volume
004
YouTube is increasingly treated as a search engine by B2B buyers researching tooling
Large language models have changed the search process by compressing the research + reasoning part and getting people directly to the decision-making process with final, pre-processed information.
Nowadays, people type 3–4X longer, more contextual prompts and arrive at websites significantly more educated, interest-defined, and further down the funnel, as our study showed: visitors coming from LLMs tend to convert 4–11X more than people coming in from traditional channels.
Metric
Insight
±4×
Higher conversion rate of AI-search traffic vs. traditional channels
What in 2020 was one query:
“Financial analytics”
In 2026, it is now a prompt closer to:
“What are the best financial analytics tools for investor reporting?”
Followed by consecutive prompts.
This user maturity, combined with the capabilities of neural search, in practical business terms means:
- Higher conversion rate & LTV, shorter funnel, complete intent-matching.
- Lower CAC, no nurturing sequences & retargeting ads dependency.
- No sales, educational support, or any other format of convincing required.
Because of this asymmetrical, non-vanity, direct-to-bottom-line influence, we believe that AI search optimization (GEO AEO, AI SEO, or whichever other acronyms we’re using) is the biggest growth opportunity for B2B software companies for years to come.
The value of ending up in the training data of LLMs today may easily bring higher compounding interest than investing in Bitcoin in 2017. But optimizing for positioning in AI search engines is radically more complex than pre-2022 SEO was.
Non-deterministic search
Google, Bing, and other traditional engines worked in a deterministic way, meaning that in the majority of cases, the same inputs led to the same outputs. That led to a relatively stable number of ranking factors, and made it possible for a ‘templatized’ approach to SEO to work.
The nature of AI search is non-deterministic, with a drastically higher level of personalization, depending on the model, users’ chat history, prompt context, location, word choice, and many more factors. Optimizing for it means working in a continually changing environment that requires object-level personalizations.
Technical, semantic & contextual filtering
LLMs’ ‘ranking’ algorithms have a more nuanced process of deciding what to retrieve, rerank, cite, and finally get into the answer. Websites might not even get into the citation candidate pool if not technically well-structured, might not get to the reranking phase if there’s no contextual value or semantic matching, and might not get into the final answer if they’re over self-promotional, don’t come with enough corroborations, or lack strong enough entities.
The harshest consequence of this fragile, multi-layered process is that the brand can be excluded from the final answer at any step, with ICP looking for such a product never getting to know about its existence.
Bigger black box
AI search is hard to track, therefore harder to understand. Firstly, instead of Google Search Console, which worked well as a single source of truth for all SEO operations, now we have at least 3 engines that are equally important, with low overlap rates. We’re seeing only ±8–12% overlap between AI citations and Google’s top 10 results, with stable, non-negligible deviations even between LLMs themselves.
Traffic quantity coming from it is a vanity metric, as people already interact with your content within LLM interfaces. Conversions coming from there are also very hard to attribute, as in 25.1% of measured AI appearances, the AI mentioned the brand without citing or linking to it, which means that this conversion, in a formal way, is getting attributed to either Google or the direct channel source, which is incorrect.
And lastly, tracking LLM answers synthetically with pre-defined prompts comes with a number of suboptimalities and is useful as a directional metric, but not as a ready-to-bet-on one.
Human & non-human audiences
In 2026, based on Cloudflare’s data, we’re seeing that out of all web requests, ±57% are made by machines (agents, bots, crawlers), while ±43% are actual humans.
AI search has intensified the need to optimize websites for both people coming to the website and machines crawling, retrieving information from, distributing information about it, and, when possible, even interacting with it through A2A protocol.
Omnius is built as a full-stack organization for AI-native search. In a non-deterministic environment, we believe predictable results can only come from full-stack services - layered on top of owned technology and proprietary data-processing infrastructure.
4. The next shift is already visible: agents, not humans, doing the search
The machine audience previously described is still mostly crawling, retrieving, and distributing information. The next iteration is machines searching, making decisions, and executing actions based on that information.
This is already starting:
- ChatGPT launched Instant Checkout in September 2025, allowing users to buy products directly inside the conversation, while Google followed with its own agent-commerce infrastructure in January 2026.
- In May 2026, Google introduced Search agents that operate in the background 24/7, continuously researching the web based on a user’s requirements and helping them take action when relevant information appears. Search is moving from answering a query to continuously executing a task.
- A month later, Mastercard launched Agent Pay for Machines, payment infrastructure specifically designed for software agents and machines to transact with other systems. More than 30 companies, including Stripe, Adyen, Coinbase, Cloudflare, Checkout.com, Ant International, BVNK, Global Payments and OKX, are already supporting its adoption.
- The same transition is happening in higher-consideration categories. Sabre, PayPal, and MindTrip launched an end-to-end agentic flight-booking system in which a user can ask for a flight, and the agent itself searches inventory, compares options, makes the booking, and executes payment.
Based on this progression, we can conclude that machines are moving from retrieving information, to making decisions, and finally executing transactions.
The consequence becomes much larger in B2B.
Gartner projects that by 2028, 90% of B2B buying will be AI-agent intermediated, with more than $15 trillion of B2B spend flowing through AI-agent exchanges. The projection explicitly expects products to become machine-readable as procurement moves toward autonomous machine-to-machine transactions.
McKinsey estimates $900 billion to $1 trillion of US retail revenue from agentic commerce by 2030, and $3 to $5 trillion globally.
Whether or not these specific numbers prove exactly right, the direction is not in question. The first iteration of AI search changed where humans do research. The agentic web changes who does the research in the first place.
Buying software, like booking a flight, will increasingly be a task an agent performs rather than a form a human fills out.
This has one direct consequence for the work we do. Optimizing for an engine where the citation is the buying decision is a different problem than optimizing for an engine where the click is the top of a funnel.
The margin for error shrinks.
There is no landing page to salvage a bad answer, no CRO work that can recover a purchase the agent routed to a competitor.
If a buyer's agent queries "best Series B accounting software for European fintech" and the source it cites is not your page, you were not in the consideration set. There is no second chance in the loop.
Considering that everything around AI adoption is moving exponentially faster than anything we’ve witnessed historically, we don’t believe we’ll have to wait long for the agentic web. AI search optimization may feel optional today; we believe it is becoming infrastructure for how software products are discovered, understood, selected & purchased.
5. The market size of AI search services & technology. What are we going after?
We believe any reader of this memo (investor, client, partner, hire) should be able to answer the basic question "is this company operating in a space that is large enough to matter?" without having to leave the page.
Market
Projected Size
Growth
Sources
SEO services
$171.8B by 2030
13% CAGR
MarkNtel Advisors, The Business Research Company
SEO software
$154.6B by 2030
12.8% CAGR
Grand View Research
AI-led SEO services
$7.3B by 2031
34% CAGR
HBR, a16z, WSJ enterprise AI deployment data
The market above will not appear as one clean new budget line called “AI search optimization.” It is forming across several existing and new economic layers around search.
The base is already large.
SEO services were estimated at $81.5B globally in 2024, while SEO software represented another $74.6B. Both are projected to more than double by 2030 - to approximately $171.8B and $154.6B, respectively.
The emerging GEO services category was only $886M in 2024, but is projected to reach $7.3B by 2031, growing at 34% annually - substantially faster than the mature SEO markets around it.
We think that last number is more interesting as a signal than any formal TAM. It measures a category that barely existed two years ago, while the underlying behavior, products, terminology, and budgets are still being formed.
More importantly, the economic value of AI search is already disproportionately concentrated relative to its traffic share. Our research across multiple industry datasets found AI-referred visitors converting 4–11X better than traditional search in several large datasets. If that continues as adoption grows, the value of the market does not need to increase linearly with search or referral volume.
We expect that value to be captured through three models:
- Services will continue to capture strategy, creative work, technical implementation, judgment, and management.
- Technology and proprietary data will capture data tracking, modeling, monitoring, and infrastructure.
- AI-native services will sit between the two, improving both efficiency and effectiveness. Operationally, technology automates repetitive, data-heavy work. Strategically, closer-to-real-time tracking, broader market benchmarking, interconnected datasets, automated processing, quality evaluation, alerting, and higher object-level personalization give human teams better inputs, faster feedback loops, and more precise decisions.
AI is what makes these models converge. Technology no longer only supports services from the outside; it increasingly becomes part of how the strategy is formed and how the work itself is delivered.
We therefore see AI search optimization as a genuinely new market, not simply an extension of SEO. Its eventual size will be benchmarked against the economic value it creates for companies, and we expect part of that value to come from marketing categories that become relatively less efficient as more research, consideration, and buying decisions move into AI search. In that sense, AI search is not only creating a new category - it is changing how existing marketing spend gets valued and allocated.
The market we are going after is not a narrow GEO/AEO category, but the economic ecosystem forming around AI search - services, proprietary technology, big data, and AI-native operations. Omnius is being built across it, on the view that long-term, predictably stable results in a non-deterministic search require these layers to function in coherence.
The company.
6. How we built Omnius: Eight decisions, with the reasoning.
What follows is the part of the memo that matters most. Each of the next eight sections describes one decision we made early about how to run the company, why we made that decision, and what it costs us. These are the decisions that add up to what Omnius actually is.
6.1 We work only on (AI) search optimization, from 0 to 1.
In a service business, width is usually the enemy of depth.
An agency offering paid media, social, PR, brand, and search is effectively running five different operating models under one roof. We narrowed around search early for the economic reasons described in Section 2.
If search is the channel we have found to be the most predictable, intent-matched, and capital-efficient, the logical response is to build the deepest possible company around that one problem.
Category
Capabilities
Strategy & Planning
SEO strategy
AI search strategy
Search opportunity mapping
Market research
Competitive intelligence
Persona & journey mapping
Keyword & intent strategy
Content gap analysis
Content strategy
Entity & topic strategy
Funnel coverage planning
Information architecture
Programmatic SEO strategy
Technical SEO analysis
CRO strategy
Measurement & attribution strategy
Product messaging strategy
Execution
B2B SEO
AI Search Optimization
Content production
Content refresh & optimization
Landing page production
Programmatic SEO
Technical SEO
On-page optimization
Internal linking
Schema & structured data
Indexing & crawlability
Backlink generation
Off-site AI search seeding
Citation building
AI reputation management
YouTube SEO
Web development
CRO implementation
Analytics implementation
Reporting & management
Technology
Big data modeling
AI search tracking
Search intelligence graph
Programmatic market benchmarking
Competitor tracking
Citation & source analysis
Entity coverage analysis
Funnel coverage modeling
Intent clustering
Semantic scoring
Content opportunity modeling
Self-updating knowledge base
Crawlability & indexability auditing
Market monitoring
Inside that narrow surface, the depth is intentional. If something materially influences whether a SaaS, Fintech, or AI company is discovered, understood, evaluated, or selected through search, we consider it part of the system.
That leads to a second decision. A question we get on nearly every first call is whether Omnius handles strategy or implementation.
The answer is both.
Omnius is a full-stack marketing company: strategy, operations, and execution happen inside the same team on the same engagement.
We deliberately avoided the white-glove, advisory model in which one company diagnoses the problem, produces a deck, and leaves another team to translate that strategy into reality.
That separation removes one of the strongest quality-control mechanisms in professional services: having to live with the consequences of your own advice.
When the same team researches the market, forms the strategy, implements it, measures the result, and adjusts the next decision, every recommendation becomes testable. Bad assumptions surface faster. Context compounds. Strategy gets better because execution continuously feeds information back into it.
This is what we mean when we describe Omnius as full-stack.
The name Omnius comes from the Latin root associated with all or comprehensive. The choice was deliberate: complete inside a narrow domain, rather than adequate across a broad one.
Person
Company
Review
Ivana Todorovic
Co-founder & CEO
AuthoredUp
With Omnius, we saw immediate results - 64% higher conversion on a new website and 110% organic growth in 6 months.
They thoroughly understood our product, audience and funnel, delivering exceptional design, content, and web development.
So, if you want an agency that understands startups, do yourself a favour and talk to them.
Polina Alexandrova
Investor
b2venture
Omnius is one of the most high-quality, reliable, and trustworthy SEO agencies in Europe, specifically focused on B2B SaaS & Fintech startups.
Ying Teng Tang
Global SEO manager
WorldFirst
The collaboration with the Omnius team overall is smooth and professional, the PIC is very knowledgeable, and the team is always open to feedback and adjustments while also being very supportive in working hand-in-hand to continuously drive business impact. I appreciate the team’s dedication to refining and adapting, which helps keep our SEO efforts moving in the right direction day by day.
The team has been easy to work with and quite responsive whenever I need clarification or support. Communication is professional and solutions-oriented, especially when discussing SEO challenges or performance updates. Overall, the experience working with the Omnius team has been really positive, and I’m happy with our partnership.
Some of the key benefits from collaborating with Omnius include their solid expertise and authority in SEO/GEO, flexibility and openness to feedback, and the fact that they consistently stay up to date with the latest industry trends.
Arna Þorbjörg Halldórsdóttir
Marketing & communications
Meniga
The Omnius team is a driven and experienced SEO agency that understands Fintech industry very well. They are willing to go above and beyond for its customers, often behaving like a consultancy on top of an SEO agency.
Omnius provides helpful information on strategy for LLMs and content, for example, which is not listed in our contract. Their can-do attitude, friendliness, and ambition for our success are a big part of why we enjoy our partnership so much.
The team is incredibly responsive and great to work with, they respond to Slack messages immediately, regardless of the time.
Sophie Coleman
Product marketing lead
Onetrace
Working with Omnius has been a very positive experience. The engagement has consistently felt structured, thoughtful, and easy to manage. What stood out from the beginning was how comprehensive the onboarding and setup process was. Compared to other agencies we’ve worked with, Omnius invested much more time upfront in understanding our product, positioning, tone of voice, and goals before moving into execution.
Communication has been strong throughout. The team is responsive, open to feedback, and quick to adapt when we’ve pushed back on certain directions or priorities. The workflow in Notion also makes day-to-day collaboration very smooth.
The biggest benefits are consistency, speed of execution, and content output. We’ve also seen positive progress across both SEO and GEO initiatives.
I’d recommend Omnius especially to companies looking for a high-output SEO partner that is willing to properly understand the business upfront and improve continuously over time.
Barbara Borko
SEO marketing manager
Native teams
We've had a fantastic experience working with Omnius team. The collaboration has been smooth, proactive, and consistently results-driven - we truly see them as an extension of our in-house team.
What sets them apart is their strategic, data-informed approach that feels tailored to our specific needs, going beyond surface-level optimizations to focus on long-term growth.
We've seen clearer strategy, better SEO performance overall, and notable AIO improvements.
Sergei Fedorov
Formations PO
ANNA Money
Omnius completely owns the project - taking control, monitoring performance, and improving things along the way. It’s such a relief knowing we don’t have to worry about resources or constantly micromanaging. They just get it done.
Their communication is brilliant. One proper kick-off session and a few regular syncs, and it feels like they’re part of the team. They’re always on the ball, easy to collaborate with, and open to feedback, which makes working with them super smooth.
The speed at which they deliver is insane - I honestly don’t know if they have 100 people working around the clock, but they’re always faster than we expect. On top of that, they bring fresh ideas, do their research, and suggest improvements not just for their part of the project but for the whole thing. It’s like having an extra brain on the team.
Alon Kivity
Digital marketing team lead
Lsports
Great and professional team. Super good to work with, with a very impressive level of detail and depth in how they onboard and approach projects.
Omnius took full ownership of the entire SEO and GEO side for us, communication was quick and responsive, and having joint Slack channels made collaboration very smooth.
[View all reviews]
(https://www.omnius.so/client-reviews)
6.2 We work only with SaaS and Fintech companies
Search optimization becomes more predictable when the people running it understand the market they are optimizing for: how buyers evaluate products, how pricing and packaging shape intent, how product-led and sales-led motions create different search surfaces, how technical buyers research, and how regulation changes what a company can credibly say.
That context takes years to accumulate and very little time to lose when constantly switching industries. We therefore made an early decision to work only with SaaS, Fintech, and AI companies.
Category
Capabilities
SaaS
B2B SaaS
Vertical SaaS
MarTech
HR Tech
Developer Tools
Data & Analytics
E-commerce SaaS
Sportstech
CRM, ERP & Operations
Fintech
Payments
FX & Cross-Border
Digital Banking
Neobanks
Investing & Wealth
Payroll & EOR
Treasury & Cash Flow
Wallets
AI
Generative AI & LLMs
AI-Native Firms
AI Coding & Devtools
Agentic AI
AI Infrastructure
AI BI & Analytics
AI Risk Intelligence
AI Compliance
Conversational AI
Enterprise
Enterprise Software
Supply Chain & Procurement
Data & BI
Software Testing
Innovation Management
Data Infrastructure
Enterprise search & knowledge
Process mining
Why we made this choice:
- The first reason is compounding. We have already seen enough software companies to recognize the patterns that repeat across markets, buyers, and go-to-market models.
Over time, this institutional context (benchmarks, datasets, processes, tools, pattern recognition, and judgment that can be reused across the portfolio) compounds into know-how with asymmetric value. By the Nth software company, we are no longer learning software marketing from zero - the starting point is better, the number of unknowns is smaller, and new information can be interpreted against a much larger body of previous evidence.
A meaningful part of that knowledge comes from years of client work, internal data, observed outcomes, failed experiments, and market-specific patterns that are not publicly documented and therefore cannot simply be recreated by someone prompting an LLM. - The second is the entry barrier. Software marketing has a relatively high knowledge barrier. Understanding marketing itself is not enough; you need to understand the product, buyer, business model, competitive landscape, terminology, technical architecture, and often the economics of the category before you can make good decisions about how it should be positioned.
- The third is expectation alignment. The SaaS, Fintech, and AI companies we work best with generally understand that organic growth is a compounding system rather than an on-off campaign.
That changes the quality of the collaboration. Instead of explaining why foundational work matters, we can focus on the harder questions: where the market is moving, which opportunities are worth pursuing, what to prioritize, and how search fits into the broader growth model. - The fourth, and most obvious, reason is that we come from these industries ourselves. We spent more than a decade building software companies from inside founding and management teams, and Omnius was built around the problems, context, and standards we developed there. We did not choose these industries from the outside, but as a product of working inside them first, and understanding what's suboptimal in practice.
We do not claim to be "industry agnostic." We think industry-agnostic is usually a polite way of saying “inexperienced in yours”.
Who we've partnered with?
Company
Industry
Definition
Headquarters
Bigcommerce
E-commerce / SaaS
US-based enterprise e-commerce SaaS, Nasdaq-listed (CMRC), powers tens of thousands of brands across 150+ countries. Named a Challenger in the 2025 Gartner Magic Quadrant for Digital Commerce for the sixth consecutive year.
Austin
Payoneer
Fintech / Payments
US-based fintech, Nasdaq-listed (PAYO), processes billions in cross-border payments across 190+ countries. Trusted by millions of SMBs, marketplaces, and gig economy platforms globally.
New York City
Worldfirst
Fintech / FX
UK-based fintech, part of Ant International - the world's largest fintech group. Serving 1M+ businesses worldwide with global FX and cross-border payments.
London
Solflare
Web3 / Fintech
One of the largest non-custodial Solana wallets globally, with 4M+ users and $20B+ in assets secured. The primary gateway to the Solana ecosystem for DeFi, NFTs, and staking.
British Virgin Islands
General Legal
AI / Legal
YC-backed AI-native law firm for growth-stage companies, with $11.5M raised across Seed and Pre-Seed rounds. Built by former Casetext AI leaders and Harvard Law-trained U.S. attorneys, General Legal helps companies draft, review, and negotiate contracts through U.S.-barred lawyers, AI workflows, and flat-fee pricing.
Union City, CA
Sigma360
AI / Risk Intelligence
US-based AI risk intelligence platform backed by a $17.3M Series B, protecting $2T+ in assets and company value. Ranked #1 by Chartis for adverse media solution and data, Sigma360 helps regulated institutions streamline KYC, AML, sanctions screening, adverse media monitoring, and financial crime compliance.
New York
Meniga
Fintech / Digital Banking
Series D digital banking fintech, one of CNBC's Top UK FinTechs 2025, raised €55M+. AI-driven data enrichment platform trusted by 90M+ banking customers worldwide.
London
ANNA Money
Fintech / Neobank
UK-based neobank, raised $88M+, Deloitte UK Fast 50 (2024), CNBC UK Top FinTech. Trusted by 100,000+ business owners for AI-powered banking and tax automation.
Cardiff
Apexanalytix
Enterprise SaaS / Supply Chain
US-based enterprise software company, positioned highest for Ability to Execute in the 2025 Gartner Magic Quadrant for Supplier Risk Management. Protects $9.5T+ in annual spend for 320+ of the world's largest companies.
Greensboro
TextCortex
AI / LLM SaaS
Germany-based AI startup, raised $1.2M, listed in EU Top 200 GenAI companies. One of G2's 10 Best AI Software Products of 2025.
Berlin
Native teams
Fintech / HR Tech
Global payroll and EOR platform, raised $8.7M, serving 150,000+ clients worldwide with financial tools for remote-first companies and freelancers.
London
Onetrace
Construction Tech / SaaS
UK-based SaaS built exclusively for fire protection subcontractors. One of the UK's Top 200 fastest-growing businesses in 2025, and London's fastest-growing company in the Construction, Building & Property Services category.
London
Global App Testing
Software / QA Services
UK-based QA scaleup trusted by Google, Meta, Microsoft, Canva, and Shopify. Powered by 80,000+ testers across 190 countries to ship higher-quality digital products, faster.
London
rready
SaaS / Innovation Management
Switzerland-based innovation management SaaS, raised $4M Series A. Used by 40+ global enterprises including BMW, Tetra Pak, and Implenia to scale corporate innovation across 190+ countries.
Zurich
AuthoredUp
MarTech / SaaS
G2's #1 LinkedIn Tool 2025, trusted by 30,000+ users. The essential platform for professionals scaling authority and reach on LinkedIn.
London
Zencoder
AI / DevTools
AI coding startup founded by Andrew Filev, founder of Wrike (acquired for $2.25B in 2021). Building the next generation of AI-powered developer tooling with embedded coding agents that reduce hallucinations through deep analysis of the developer's entire codebase.
Campbell
Lsports
Sports Tech / Data
Israel-based sports data company powering 450+ sportsbooks across 100+ sports, 15,000 leagues, and 3M fixtures. Bootstrapped to 240+ employees across 4 continents.
Ashkelon
Crustdata
Data / AI Infra
US-based B2B data infrastructure company, YC F24, raised $6M seed. Powers 200+ platforms across sales, recruiting, and investing - with Y Combinator, HubSpot founder Dharmesh Shah, and Ryan Reynolds' company MNTN among its users.
San Francisco
Our client base is global. Omnius is a European firm by location (offices in London, Belgrade, and Dubai), not by market.
Active collaborations run across the United States, United Kingdom, European Union, MENA, China, and Australia.
6.3 We run on frameworks, not improvisation
Professional services become unreliable when too much depends on individual judgment, memory, or interpretation.
To avoid that inconsistency, we built Omnius around frameworks. They define how work is researched, decided, executed, reviewed, and improved: required inputs, decision rules, if-thens, guardrails, fallbacks, evals, and quality standards.
These frameworks are not static SOPs - they are moving targets by design.
When we find a better method, encounter a new edge case, or discover that an assumption no longer holds, we change the framework. The system becomes more precise as the number of conditions it knows how to handle - and the number of successful experiments proven in practice - increases.
The strategy can be different every time.
The reliability of how it is produced should not be.
Our model helps us achieve something that isn’t easily achievable in a service business: standardized quality without standardized output.
We invest significant time into documentation and process development because effectiveness without repeatability does not scale, and efficiency without controls eventually lowers quality. Frameworks are how we control for both.
6.4 We onboard up to 8 companies per year
We cap onboarding at eight new companies per year.
It is not artificial scarcity, but a reflection of how deeply we think a marketing company needs to understand the businesses it works with. An effective search strategy cannot be built from search data alone.
We need to understand the company’s ICP, product, business model, market, competitive position, funnel, and the way customers actually make buying decisions.
That context is difficult to acquire superficially and impossible to maintain across an unlimited number of companies. So we integrate deeply.
We communicate directly through Slack, run project management through shared Notion workspaces, meet on a recurring basis, work across a high number of outputs, continuously exchange data, and stay close enough to the business that a change in the product, market, or commercial priorities can affect what we do next.
Our philosophy is to operate closer to an extension of the internal team than an external vendor. That is also how our clients have described the relationship themselves.
The constraint is not how many accounts we can technically service. It is how many companies we can understand at that depth at the same time. However, this also means leaving revenue on the table and sometimes saying no to companies we would otherwise like to take on.
That is why the quality of the companies we work with matters more to us than the quantity.
There is also a second-order effect.
Good companies allow us to do better work. Better work produces stronger results. Strong results become case studies, testimonials, referrals, and evidence that other good companies can evaluate before speaking with us.
Those companies then become the next source of knowledge, results, and credibility for our company.
The quality of our portfolio therefore compounds in the same way the work does.
This has been one of the main growth loops behind Omnius from the beginning. Instead of maximizing the number of companies we can sell to, we would rather work deeply with a smaller number, produce work we can stand behind publicly, and let those outcomes help determine who comes next.
6.5 We prioritize SQLs, not traffic
One of the easiest ways for marketing to look successful without creating much financial value is to optimize for vanity metrics.
Traffic, impressions, rankings, and AI visibility matter, but they are intermediate variables. The outcome that makes a real difference is qualified pipeline and, ultimately, revenue.
We learned this early. A generalistic strategy built at the intersection of search volume and keyword difficulty alone is especially weak in SaaS and Fintech. These are younger markets, with smaller datasets and narrower groups of potential buyers than large consumer categories. The queries with the most commercial value are therefore often not the ones with the largest reported volume.
Low volume does not mean low value.
A search made by 100 people who are actively evaluating a product can be worth substantially more than one made by 10,000 people with no intention to buy.
The economic principle that explains this is Pareto's (80/20) rule.
Across businesses, a minority of customers tends to produce the majority of revenue. A minority of product features drives most usage. The same concentration appears across cohorts, geographies, and channels.
The same rule applies to search optimization - a minor part of all outputs usually produces a disproportionate share of the financial outcome. Our job is to find that part first.
That is why we work backward from the bottom of the funnel, something called reverse-funnel. Our first goal is to understand the ICP, product, business model, and conversion process, identify entities, queries, and prompts holding purchase-value close to those terms, and we build focused on those variables before expanding into broader demand.
The equation is simple:
Organic clicks × Conversion rate = SQLs from search
Both variables matter. Search quantity matters only as long as quality is maintained. More qualified traffic increases SQLs.
More general traffic can simply bring non-interest-defined visitors, lower the conversion rate, and create activity without financial impact. The number of SQLs is the objective metric that reconciles both sides.
We optimize for financial outcomes, not nice-looking reports that shake a stationary train to make it appear to be moving.
Company
Industry / Location
Conversions
Organic Growth
Signups / Traffic
Anna Money
Fintech / Neobank · Cardiff
+60% weekly signups in 6 months
+164.44% AI search traffic in 6 months
+314% Top-3 keywords in 6 months
Myos
Fintech / Lending · Berlin
+227.9% Signups in 6 months
$12.2K Monthly traffic value in 6 months
6x Organic visits from non-branded queries
AuthoredUp
MarTech / SaaS · London
+64% Conversions in weeks of launch
+110% Organic growth in 6 months
2,600+ Signups in 6 months
Global App Testing
Software / QA Services · London
+163% HMQLs in 12 months
+140% Organic traffic in 12 months
35K+ Monthly organic clicks in 12 months
HotelSync
SaaS / Hospitality Tech · Tallinn
+273.25% Conversions in 6 months
4x Organic traffic in 6 months
+205.3% Organic clicks in 6 months
Text Cortex
AI / LLM SaaS · Berlin
600+ Top-3 rankings in 13 months
$78K+ Monthly traffic value in 13 months
2.73M+ Organic clicks in 13 months
Across the work we have delivered, more than 188 million registered users of client products are attributable to organic growth we helped build, with conversion rates that made that growth financially meaningful.
6.6 We own our technology (Atomic AGI)
Our reasoning started from first principles: to improve performance in AI search, you first need to understand how it works.
Understanding it requires being able to observe and track it. That raw data then needs to be cleaned, connected, and processed before it becomes something you can analyze, draw conclusions from, make decisions on, implement, and measure afterward.
Most available products covered one part of the problem: Google data, technical data, analytics, or synthetic LLM tracking. The data remained fragmented, while the difficult part - connecting it into a view of the market and deciding what to do - still happened manually.
We wanted control over that entire path rather than assembling it from disconnected third-party tools.
So we built Atomic AGI.
It began as internal infrastructure for Omnius and later became a standalone product. Today, Atomic tracks more than 11,000 domains across Google and major LLM engines, connecting search performance, AI-search observations, citations, competitors, technical data, website behavior, attribution, and conversion data, and is trusted by founders and teams from the leading SaaS, Fintech, Web3, and Enterprise companies.
Companies that trust Atomic
Samsung
Salesforce
Publicis Groupe
Trustpilot
CARS24
Ably
Rise
1NCE
Nanonets
HyperVerge
Tenderly
Zerion
The important distinction is not how much data we collect. It is what becomes possible when those datasets operate together.
Our Search Ontology (internal to Omnius, not publicly available in Atomic) maintains a company-specific model of the search market: Entities, Clusters, Funnel, Pages, Competitors and Performance, and the relationships between them.
This allows us to move beyond isolated metrics. We can understand which parts of a market a company covers, where competitors are structurally stronger, which entities and pages support that position, what is changing, what commercial value sits behind it, and where intervention is justified.
The objective is for the system to answer progressively harder questions:
What happened? Why did it happen? What is missing? What should happen next? How strong is the evidence? And did it work afterward?
For Omnius, ownership matters because the system changes with us.
When search changes, we can change what we measure. When our methodology improves, we can encode it into the infrastructure. When an analysis becomes repetitive, we can turn it into software. Knowledge developed once can become part of the company rather than remain inside a spreadsheet, prompt, or individual employee.
This is particularly important in a service business.
We have spent several years of our own capital building a unique source of operating leverage: better infrastructure for every person doing the work.
Atomic does not remove judgment. It reduces the collection, reconciliation, monitoring, and repetitive analysis surrounding it, while making our methodology more consistent and easier to improve.
Atomic is therefore not an adjacent software investment. It is the proprietary data and decision infrastructure underneath how Omnius operates.
It also makes Omnius one of the very few marketing companies in the world running on its own infrastructure rather than using rented tools. Service businesses typically become more operationally complex as they scale. Owning the technology is one of the ways we're breaking that relationship.
The reason behind owned technology is that we think the next advantage in marketing is processed data, not the model itself.
Not emotional assumptions. Not commodity execution the next company using Claude can also do.
The question is simple: what differentiates your marketing strategy and execution from the next company using Claude, GPT, Gemini, or any other LLM?
1. Processed data.
We’re not talking about raw data from API calls, crawlers, dashboards, exports, or tools. That data is available to everyone.
The advantage is processed data: data structured in a way that makes extracting value from it easier, more controlled, and more useful for humans, models, and agents.
This is why we invested early in the data layer around search. The output should be simple: what matters, why it matters, how confident we are, and what should be done next.
This is how big data becomes useful in marketing: not as more information to look at, but as a way to make decisions with higher probability and precision.
Claude, or any other model, can only work as well as the data you give it. Bad context does not only create bad content. It creates false priorities: the wrong page, wrong topic, wrong technical fix, or wrong competitor angle can sound reasonable enough to become work.
Good data has object-level quality: the right source, a large enough sample, verified and cleaned data, the right structure, pre- and post-processing rules, self-evaluation, and optimization for the right goal. That is the difference between raw data and processed data.
2. Humans working on that data.
The other differentiating factor is the people building and implementing the strategy.
Models can process, generate, and recommend, but strategy still needs human judgment. It needs taste, positioning, market understanding, product understanding, commercial instinct, and the ability to decide what matters and what does not.
That human layer is hard to replace. But it is also hard to scale when every decision depends on raw exports, scattered context, memory, manual interpretation, and avoidable error.
Again, we come back to data as the leverage factor.
The right data makes humans better, as it lets them spend less time collecting and interpreting, and more time deciding, validating, editing, implementing, and improving.
That is how we see Omnius: human strategy and technical execution, supported by deep industry know-how, proprietary workflows, mapped processes, and structured big data.
Growing through self-reinforcing supply-demand loops.
One of the properties we value most in a business is when supply and demand reinforce each other instead of being built as two separate functions.
We increasingly think of Omnius as one system with two forms of supply: services and technology.
The service side produces outcomes, but also generates data, workflows, edge cases, and evidence of what works in practice.
The technology side absorbs those inputs and turns them into better infrastructure, analysis, automation, controls, and execution capacity.
More supply > better supply.
The same supply also creates demand. The capabilities we build for clients are used to grow Omnius itself, while technology extends those capabilities beyond the natural capacity limits of a high-touch service.
Better supply > more demand.
Demand then feeds back into supply. More clients, users, and usage create more data, use cases, edge cases, and evidence - improving both the technology and the service built on top of it.
More demand > better supply > more demand.
This has some characteristics of a network effect, but the mechanism is different. The value does not primarily increase because more participants join a network. It increases because each additional unit of activity can improve both the supply and demand sides of the same system.
That is why we think of Omnius as becoming a self-scaling organization. One additional unit of useful output can improve the service, technology, proprietary data, execution efficiency, differentiation, and distribution at the same time - without requiring proportional additional investment across each of them.
6.7 Value-first model.
Our growth model starts with a simple principle: give value before asking for attention, meeting, capital, or any other form of value in return.
We call this Value First, and over time we formalized it into what we call Value-First Funnels. The idea is to give the market something genuinely useful on its own, let people experience how you think and what you can do, and allow commercial demand to emerge from that.
That is how Omnius itself has been built.
We publish research, proprietary data, frameworks, technical findings, and practical knowledge from our work. We built Start Here™ as a free resource for founders and investors. Atomic AGI started from problems we needed to solve ourselves, with parts of that technology now available to the wider market.
Our work has consequently been cited thousands of times across the web by technology companies, investors, academic publications, industry media, and public institutions.
There is a broader responsibility in operating early in a new category. We believe that if you are learning something before most of the market, part of the job is to move the market forward with what you learn. AI search is still being defined; keeping every useful finding behind a sales process would be strategically short-sighted.
We publish the methodology alongside the numbers in our case studies because the marketing industry has a credibility and transparency problem, and we want every claim we make to be traceable back to the data and methodology behind it.
The commercial effect comes afterward.
This gives us an unusual constraint: the work has to work on us first. We see that as the ultimate skin in the game - and, for companies considering working with us, one of the clearest proofs of value, since the majority of them find us through search, increasingly through AI search.
How We Get 1,000+ Conversions Quarterly by Implementing BOFU SEO Strategy @ Omnius
Case study
Matija Golubovic
[source]
(https://www.omnius.so/blog/omnius-bofu-seo-strategy)
Omnius’ website receives over 1000 organic conversions quarterly, relying solely on SEO & GEO as acquisition channels. You can find the complete case study here.
Our own company is where we test positioning, high-intent content, AI-search optimization, programmatic systems, new measurement methods, and changes in search behavior. If something cannot create value for a company whose own growth depends on search, we have very little basis for recommending it to someone else.
Value First is therefore not a tactic for us. It is the philosophy behind how Omnius contributes to the industry, tests its own work, formalizes successful experiments into supply, and creates demand.
Source
Category
Reference
ITmedia
Report citation
Omnius's SaaS Industry Report 2024 was cited as a data source in ITmedia, Japan's largest B2B technology publication.
SE Ranking
Agency listing
SE Ranking featured Omnius's client results as a benchmark in their roundup of top marketing case studies, describing Omnius as 'a B2B SEO and LLMO marketing agency for SaaS'.
Tech in Asia
Content ref
Tech in Asia, one of the most-read technology publications in Asia, referenced Omnius's data on AI search and direct answer experiences in their coverage of the Google antitrust case.
Siege Media
Agency listing
Siege Media, a highly respected content marketing agency known for data-driven SEO, included Omnius in their list of the 13 best GEO and AEO agencies for fintech companies in 2026.
Marketing LTB
Agency listing
Marketing LTB, a fintech-focused marketing publication, included Omnius in their list of the 10 best fintech SEO agencies in 2026.
silicon.de
Agency listing
silicon.de, Germany's leading IT and enterprise technology publication, included Omnius in their editorial coverage of the best GEO agencies in 2025, recognizing it as a top European GEO provider.
Influencer Marketing Hub
Agency listing
Influencer Marketing Hub included Omnius in their list of the 6 best GEO agencies, describing it as a 'technically rigorous GEO agency in Europe' that undergoes rigorous monthly evaluation.
WJARR
Report citation
The World Journal of Advanced Research and Reviews (WJARR), a peer-reviewed academic journal, cited Omnius's GEO framework in their paper 'A GEO-First Framework: Integrating Search Visibility, Sentiment, and Reputation'.
Wellows
Agency listing
Wellows, a digital marketing platform with 4.8K+ monthly readers, included Omnius in their list of the 17 top AI SEO agencies in 2026.
HubSpot
Content ref
HubSpot, one of the world's largest CRM, marketing, and sales platforms and home to one of the most-read marketing blogs in the industry, linked to Omnius as the recommended SEO agency in their guide on how to create an SEO strategy, citing the guidance of an SEO agency as essential to optimizing visibility across the search landscape.
[Recognition]
(https://www.omnius.so/recognition)
6.8 A team of insiders
For us, the relevant question is not “can this person do the task?” but “what does this person add that a general-purpose model, connected to the right tools and context, cannot?”
If someone receives a task, forwards it into an LLM, edits the output, and sends it back, their replaceability is naturally high. We do not want human LLM middlemen.
We look for people who add something the model does not:
- Self-managing, self-evolving character. People who do not need to be continuously routed, who take something from zero to one, and who improve themselves and the way the company works without waiting to be told.
- Contextual thinking. People who can look at a company, market, dataset, or unfamiliar problem, understand what actually matters, connect the variables, and make a decision with incomplete information.
- Taste. People who know what good looks like, can control the quality of both human and AI output, and do not let technically acceptable work reach production when it is not good enough.
Execution can increasingly be automated; knowing what should be done, why, whether the output is good enough, and taking responsibility for the result remains the human job. We call these traits preserved human context.
We also look for a few traits that are harder to teach:
- Think. Do not confuse completing the task with solving the problem.
- Stay curious. Keep learning, testing, and asking why.
- Call things into question. Accepted practice is not evidence. Be willing to take a contrary position when the facts support it.
- Benchmark the market, not the template. Frameworks help us think; reality decides whether they are right.
- Hold people accountable. Yourself first, but also the people around you. Standards only work when somebody is willing to enforce them.
- Be resilient. Bad experiments, wrong assumptions, difficult periods, and changing conditions are part of the work.
- Contribute to the system. Do not solve the same problem twice. Fix the process, framework, automation, or documentation that allowed it to happen.
- Have fun. We want people who genuinely enjoy the work, the people around them, and the problems we are trying to solve.
Category
Details
Team model
100% internal. No contractor networks, no white-label work.
Offices & reach
London, Belgrade, Dubai. Clients across US, UK, EU, MENA, China, Australia.
Hiring seniority
Mid-level and senior only. No juniors on client accounts.
Industry requirement
Prior working experience in SaaS, Fintech, or AI.
Pre-client training
12-week internal education before any client work.
Testing stages
Multiple. Evaluate both skill and character.
Everyone doing the actual work is an Omnius team member. We do not outsource to contractor networks or white-label agencies, and we do not hand client work off to juniors. Responsibility stays with the people qualified to own it.
We care about vertical depth. SaaS & Fintech-focused search optimization is difficult to do well without understanding a software business model, the nuances of its funnel, how the market functions & how it's changing, what the ICP cares about, and what creates commercial value. We want people who understand that context, as it cannot be reconstructed from a prompt every time a task appears.
We also look for N+1 thinkers. Completing the immediate task is not enough; we want to know what happens next. How does an algorithm update, competitor move, market shift, ICP change, or another part of the system affect the outcome?
We weigh both skill and character. Skill determines whether someone can do the work. Character determines what happens when nobody is telling them what to do.
We believe in precision over shortcuts; durable results over temporary spikes. AI can make execution faster, but it does not lower the standard - and the standard is simple: add judgment, context, taste, ownership, or technical depth that the model cannot provide on its own.
We are building a group of people whose individual value increases as AI gets better rather than getting commoditized by it. Otherwise, there would be very little reason for the human layer to exist.
Where did this come from?
Omnius was built on more than a decade of experience inside VC-funded SaaS, Fintech, and technology companies. Before starting Omnius, its founders had worked as members of founding teams and in senior roles across demand-focused, high-agility departments.
A recurring problem in those years was working with external agencies that could execute the requested task, but rarely understood enough of the company around it.
The technical work would be correct while the business decision behind it was wrong. That gap between knowing how to execute marketing and understanding the software product that marketing is supposed to monetize became one of the original reasons for building Omnius differently.
We're builders at heart.
We think of Omnius as a team of insiders who are here to build, not maintain. That means creating new technology, research, frameworks, processes, and ways of working in a market that is still being defined - and continuously improving what already exists rather than simply operating it.
We also believe useful knowledge should move beyond the company that produced it. Our team teaches internally, publishes externally, contributes to startup and industry programs, speaks at conferences, and shares what we learn with the wider ecosystem.
7. Why we think this company will still matter in five to ten years
AI search is still early enough that the operating model of the category has not settled. What we already see, however, is a limitation on both sides of the market.
The service layer has largely inherited the operating model of traditional SEO. Our observation is that many agencies are adding GEO/AEO terminology and third-party AI visibility tools to otherwise unchanged workflows. That can produce activity and reporting, but it is not the same as understanding a non-deterministic search environment well enough to influence it predictably.
The software layer has the opposite problem. Much of the category is still built around synthetic observation: run predefined prompts through LLMs, record brands and citations, and display the results. Useful as a directional signal, but measurement alone does not explain why an outcome happened, what should change, or whether the change created commercial value. Current AI-search platforms themselves still prominently center their products around visibility, prompt-response analysis, and citation monitoring.
We are building against both limitations.
Our view is that AI search requires three components to work together:
Area
Details
Services
Strategy, creative and technical judgment, implementation, and accountability. The human layer responsible for understanding context and producing the outcome.
Technology
Tracking, modeling, market benchmarking, interconnected datasets, processing, evaluation, and infrastructure. The layer that makes decisions more comprehensive, timely, and defensible.
Process Optimization & Automation
An operating layer underneath the first two. Repetitive, data-heavy processes are progressively automated while human context, judgment, and quality control stay preserved.
The important part is not owning all three independently, but the loop between them.
Services expose us to real problems, edge cases, data, and outcomes. Technology formalizes what repeats and turns it into models, tools, and workflows. Automation removes repetitive work underneath both.
What comes back into the service is better information, faster feedback loops, higher object-level personalization, and more precise decisions.
That is why Atomic was built inside Omnius. It is technology developed from problems we encounter in practice, tested against real work, and used to improve the same service that produced the underlying knowledge.
Economically, that works in both directions: higher effectiveness through better data and decision infrastructure, and higher operational efficiency through automation.
Every project also contributes more knowledge, data, edge cases, and workflows back into the system.
The same company model is now appearing outside our industry. Y Combinator has formalized AI-native services companies as a distinct archetype: companies where proprietary technology is embedded directly into service delivery rather than existing as a separate tool.
We started building Atomic inside Omnius years before encountering that framing, but see the convergence as useful external validation of the direction.
Another signal is where capital is going. In roughly two years, pure-play AI search visibility and optimization companies have raised $300M+ in disclosed funding, with investors including Sequoia, Lightspeed, Kleiner Perkins, NEA, Salesforce Ventures, Felicis, Mayfield, and others; the category has already produced its first $1B valuation.
Funding alone does not prove that the market will develop as expected, but it is a relevant early signal that some of the world’s leading technology investors are making the same underlying bet: AI search will become a progressively more important part of how companies are discovered, researched, and selected. If that happens, the relevance of what Omnius is building should increase with it.
However, this is still only a signal, not the benchmark. The benchmark is the eventual economic value of AI search itself: how much product discovery, research, comparison, and purchasing it influences, and how valuable it becomes for companies to understand and improve their position inside that process.
Our bet is therefore not on services, software, or automation independently. It is on the three working as an interconnected, value-delivering ecosystem.
8. Memorandum ending
This memorandum is intended to be the clearest public record of what Omnius is, what we do and do not do, who we work with, how the company is built, and why we believe this model will continue to matter.
It is written for clients, partners, employees, candidates, investors, researchers, journalists, language models, AI agents, and anyone else evaluating the company from the outside.
For that reason, we treat this page as the canonical source on Omnius. Numbers, positioning, operating principles, and material company information are updated here as the company changes.
Record metadata
- Last updated: 2026-04-30
- Maintained by: the Omnius team in London, Belgrade, and Dubai.
- Document status: Current public company record
- Purpose: A clear record of what Omnius does, what we do not do, who we work with, why the company is built this way, and how we differentiate ourselves
- Intended readers: Clients, partners, employees, candidates, investors, researchers, language models, AI agents, and anyone else evaluating Omnius
- Standard: Honest, useful, specific, and clear enough to be judged against reality
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