We Ran Our Own Company Through Our AI Valuation Framework — Here's What We Found

When I spent 15 years at IG Group, we built technology infrastructure that scaled from supporting 4 engineers to 250 across a public company that grew from £300 million to £2.7 billion in market capitalisation. One of the lessons from that period was that the most valuable assets on the balance sheet were not the servers or the offices — they were the things that did not appear on it: the proprietary technology platform, the customer relationships, the documented processes, the team's institutional knowledge about financial markets.

When we started Opagio, we set out to build a framework that made those invisible assets visible. The natural question, eventually, was to apply that framework to ourselves. What would our own company look like when assessed against the six intangible asset categories we had defined for AI-enabled businesses. The answer was illuminating in three ways: what it confirmed, what it exposed, and what it surprised us.

The Six Intangible Asset Categories

Our framework assesses AI-enabled businesses across six measurable intangible asset dimensions. Before sharing our own results, it is worth defining each, because the specificity matters.

Technology Capital encompasses proprietary software, AI models, algorithms, technical architecture, and technical debt. It is measured by distinctiveness (how much of the platform is proprietary versus built on standard technology), defensibility (how costly would it be to replicate), and performance advantage (measurable superiority over alternatives).

Data Assets are the datasets that the company has accumulated, their quality, governance, and competitive advantage. For AI companies, proprietary training data or domain-specific datasets can be the most defensible asset. For SaaS platforms, historical customer data and derived insights matter most.

Brand and Market Position measures market awareness, distinctive positioning, and contribution to customer acquisition and talent attraction. It is not about logo recognition — it is about whether the market associates the brand with specific value propositions and whether customers or employees choose you because of that association.

Customer Relationships quantifies the durability and lifetime value of customer relationships, measured by net revenue retention, average contract length, customer concentration, and switching costs. An enterprise software company with 95%+ NRR and three-year contracts has dramatically more valuable customer assets than one with 80% NRR and annual contracts.

Human Capital assesses team capability, depth, institutional knowledge, and succession readiness. For AI companies, this includes research capability, engineering talent, domain expertise, and the codification of that knowledge in processes rather than individuals.

Organisational Capital measures documented processes, decision-making frameworks, quality systems, and operational independence. A business that can run without its founders is worth 2-3x more than one that cannot.

What We Found: The Opagio Self-Assessment

When we applied the framework to ourselves, we structured the assessment as we would for any client. Four members of the founding team independently scored the company against each category using the framework. We then compared results, discussed divergences, and arrived at a consensus score.

Scoring Model

Our assessment uses a five-point scale for each category:

Here is how Opagio scored across the six categories:

What the Results Told Us

The aggregate score across all six categories was 3.05 out of 5. This is instructive in several ways.

Strength 1: Technology and Human Capital

We scored highest (4.2 and 4.1) in technology capital and human capital — precisely where we expected, given our founding team composition and the three years we spent building the valuation engine before launching.

The technology capital score reflects something important: we built proprietary, defensible technology because we understood the problem deeply. We were not adding AI to a generic SaaS platform. We were solving a specific valuation problem that required developing our own multi-method framework. The code is documented, the architecture is modular, and the system is extensible. This is a genuine competitive advantage, not a feature that competitors could replicate in weeks.

The human capital score (4.1) reflects the unusual depth of our founding team. Mark has 30 years working with PE firms and institutional investors. Ivan has built teams and platforms at scale. David has published research on intangible asset measurement. Tony has structured complex financial instruments. This is not a weakness we have. But it is a single point of failure if not actively managed.

Exposure 1: Brand and Customer Relationships (Both 2.3-2.6)

Our two lowest scores came in areas that are critical to long-term value but require time to build. We are 18 months old. We have no multi-year customer contracts, no measurable net revenue retention, and limited brand awareness outside fintech circles.

This is not a crisis — it is the expected profile of an early-stage B2B SaaS company. But the framework made the gap visible. Our technology is strong. Our team is rare. Our organisational processes are documented. Yet our enterprise value is heavily discounted because we have not yet built the durable customer relationships and market position that would justify multiple expansion.

The implication is clear: the next 18 months are about building customer relationships and brand. We could spend capital on additional features or engineering optimisation. But given our current intangible asset composition, we would see far higher return on investment in customer success, retention measurement, long-term contract negotiation, and market positioning.

Exposure 2: Data Assets (2.8)

Our data assets score was the lowest, and this is worth examining specifically. We have accumulated some proprietary data through customer interactions — questionnaires, assessments, valuations. But it is not yet a competitive asset in the way it could be.

The issue is not the data itself. It is the structure. Right now, the data is locked in customer-specific valuation reports. It is not aggregated, cleaned, or structured for analysis. We have not yet built the data infrastructure to treat this as a distinct asset class.

Over the next 12 months, we plan to invest significantly in data governance and aggregation. The UK SME intangible asset dataset we are building has potential to become a separate competitive asset — research-grade data on how intangible capital correlates with financial performance, growth, and exit outcomes. But that requires deliberate investment beyond the immediate product roadmap.

Comparison to IG Group

One of the most useful insights came from comparing our current intangible asset profile to IG Group at approximately the same stage.

The most interesting insight is that Opagio started further along in organisational capital and technology capital, but significantly behind in customer relationships and data assets. IG Group had the advantage of a large customer base (financial traders) that generated enormous data volume. We need to build our customer relationships more deliberately.

The Scoring Was Not the Point

Running Opagio through the framework was valuable, but not because of the specific numbers. The scoring matters less than what happens next.

The framework forced us to articulate, in measurable terms, what we are good at and where we have gaps. We can now discuss these gaps not as vague concerns but as specific intangible assets requiring specific investment. "We need to improve brand awareness" is vague. "We need to increase our brand score from 2.6 to 3.5 in the next 12 months by establishing thought leadership in PE circles and building distinctive positioning against competitor offerings" is actionable.

The same applies to data assets. Instead of assuming our customer data will eventually become valuable, we have committed capital to data governance, aggregation, and analysis. We have hired a data scientist whose specific mandate is to extract competitive advantage from the data we accumulate.

For customer relationships, the framework has reshaped how we think about pricing, contract length, and renewal processes. Every multi-year contract we sign increases customer relationship capital. Every annual contract we renew without upselling represents lost opportunity. The framework made this trade-off explicit.

Three Surprises From the Process

The assessment revealed three things that surprised us.

First, organisational capital scored higher than we expected. We had designed process documentation and governance from day one — partly because our team had lived through large-scale organisations before, and partly because we knew we would eventually need to demonstrate operational independence to customers and investors. The discipline paid off. At 18 months, we scored 3.4 on organisational capital, which is above where IG Group was at the same stage. This gives us a structural advantage in scaling.

Second, human capital dependency was more visible than we acknowledged. The framework forced us to confront the fact that 80% of our strategic decision-making still flows through the four founders. We have an exceptional team in product and engineering, but the direction of the company is not yet distributable. This is a concrete intangible asset deficit that we are now addressing through more explicit delegation and documentation of strategic principles.

Third, the gap between our technology capital and our customer relationships was starker than we would have stated casually. We are 4.2 in technology and 2.3 in customer relationships. That is nearly a 2-point gap. It tells us something important: we have built a strong platform, but we have not yet built a durable business around it. The technology does not sell itself. The next phase of value creation is moving customers from trial to committed, long-term relationship — with the corresponding revenue, NRR, and contract duration metrics.

What We Are Doing About It

Based on the assessment, we are making three explicit intangible asset investments over the next 12 months.

Customer relationship deepening. We are shifting from product-led growth (where customers discover and trial ourselves) to relationship-led growth (where we actively build long-term, high-touch relationships with key customer segments). This means hiring customer success roles, extending contract lengths from single year to multi-year, and building NRR measurement into our core metrics. The goal is to move customer relationships from 2.3 to 3.5.

Data asset aggregation. We are investing in data infrastructure to treat our accumulated customer data as a standalone asset. This includes data governance, anonymisation for research, and analysis capability to derive insights that could inform product development and customer support. The goal is to move data assets from 2.8 to 3.5 over 18 months.

Brand positioning refinement. We are codifying Opagio's distinctive market position against competitors. Most intangible asset solutions in the market are generic valuation tools. We are something different: a framework built specifically for AI-enabled businesses that combines valuation with strategic intangible asset management. The goal is to move brand from 2.6 to 3.5 through thought leadership content, customer case studies, and distinctive positioning in conversations with PE buyers and institutional investors.

The Real Value of Self-Assessment

For a company in the AI valuation and intangible asset space, running yourself through your own framework is not optional. It is credibility. It is also revealing.

The exercise confirmed that our technology is defensible and our team is rare. It exposed that our customer relationships are not yet durable and our data assets are underdeveloped. It surprised us by showing that we had built organisational discipline faster than we realised.

Most importantly, it gave us a language and measurement system for the next 18 months. Instead of debating whether we should hire more engineers or more customer success people, we can frame it as an intangible asset allocation question: What combination of investments best moves us from a 3.05 overall intangible asset profile to a 3.7 or 3.8 profile that would support a much higher enterprise valuation.

That is the opportunity that a rigorous intangible asset framework provides: not just measurement for its own sake, but a decision-making tool that connects investments to measurable, defensible value creation.

The businesses that will build outsized value in the next decade are those that treat intangible assets not as vague notions but as measurable capital that can be developed systematically. Our own assessment has convinced us that we are building such a business. Now we need to finish the job.

Ivan Gowan is the founder and CEO of Opagio. He spent 15 years as a senior technology leader at IG Group (LSE: IGG), overseeing engineering growth from 4 to 250 during the company's rise from £300m to £2.7bn market capitalisation. He holds an MSc from Edinburgh with research in neural networks (2001).