The discipline that runs the world’s largest investment processes had never reached the people funding its startups.
The structure and discipline of institutional investing, brought to deal flow that had neither: a business, and its financials, broken into data an investor can filter, and judged with the rigour a real investment demands.
Location / capital-markets photographyThe market has long agreed that an investor’s edge lies in deal flow: see the best businesses first and the rest follows. An entire category of software has been built on that belief, relationship-intelligence platforms that mine a firm’s network for the shortest path to a founder, lift deal information out of email, and keep a pipeline tidy. All of it organises what surrounds the deal, under punishing volume: in the UK alone, businesses made over 570,000 growth-funding applications in a single year, a single venture capital firm around 1,492 of them.
What none of it touches is the business itself. Screening the actual company is still a person reading a pitch deck, on average about ten minutes, most settled in less, weighing market, product, team, traction, model, competition, financials and exit at once, each read out of a document written to say what the founder believed mattered. The businesses that survive tend to be the ones that pitch well, which is not the same as the ones that are good. The newest answer, AI reading decks to extract the facts, still works around the deal after the fact rather than asking why it arrives in a form nothing can use.
- Interviewing
- Surveys & questionnaires
- Desk research
- Stakeholder validation
- Verification & triangulation
- Framework development
- Market & landscape analysis
- Solution & mechanism design
- Investor alignment
- Tool & model building
Built on real research. Not usage metrics.
1 Built on 200+ funders consulted on what they need to see and 25 VC application processes analysed.
It was never a problem of volume. It was a problem of structure.
The deal itself was never structured: it arrives as free-form prose, and prose cannot be sorted, scored or compared except by a person reading each one end to end. Where there is no structured data there are no fields to filter on, no common basis to set one business against another, nothing to rank, weight or audit across a pipeline. Deal flow is not unmanageable because it is large or badly sourced; it is unmanageable because the thing being judged has no structure.
So the task was never to help investors source better or read faster. It was to give the deal a structure, so the investor receiving it could weigh it against their own criteria with the discipline they would bring to any other investment. Bringing structure to how those decisions are made is exactly the kind of problem we are hired to solve for others. iDeal is what happened when we built the investor’s side of one of our own.
How we pulled it together
We worked out what a business can actually be broken into.
Structuring a business as data sounds simple until you have to decide what a business consists of and which parts can be pinned down. That analysis is the work, and almost no one does it. Take the market a company is chasing: quoted as a value or a number of customers, more or less at random. We broke it into value and volume, defined each, and made both filterable; did the same to revenue, split by type, and to the whole financial picture. We went through hundreds of real application forms to do it, and found almost all capturing information consistently but not to a grain anyone could filter.
We brought the discipline of institutional analysis to the earliest stage.
The same rigour, turned from what a business is to how it gets judged. Investment processes at scale make every analyst record the same things in the same shape, and grade on even-numbered scales that force a real position rather than a safe midpoint; we built both in. A serious manager also gathers several views on the same company and keeps them, so agreement and dissent can be seen over time. We gave a venture committee exactly that: every view on a deal captured, comparable and on the record. None of it existed at a stage that had only ever run on a ten-minute read and a feeling.
We built the discipline to be extended, not just applied.
A structured system usually forces a choice: stay rigid, or let people bolt on freeform fields and watch the structure leak away. We refused it, and built the ability for an investor to define their own filtering categories under the same discipline as the core ones, properly defined, typed and matchable, so a criterion a fund invents for its own thesis is as structured and screenable as anything we set. The method was never a fixed set of fields; it was a discipline an investor could carry into their own terms without losing any of it.
What we delivered
iDeal drew on a mix of expertise that rarely meets in one place: a first-hand command of how institutional investment processes actually run, the analytical work to break a business into structured, filterable data, the research to ground it in what real funders need, and the technology to build it into secure, scalable software. The work was owned by a small senior team, each responsible for the part they knew best.
Thierry Clarke led, carrying the investment-process discipline at the centre of the product and the data-structuring thinking behind it, both drawn from years inside institutional investment management. Tsvetelina Zapryanova, a Commercial Partner at InvestorConnected, ran the research and the direct outreach to the funder community the whole approach rested on. Jeremy Hunt brought the financial and accounting expertise, a Chartered Accountant whose command of financial reporting meant the financial side of a business was broken down the same way as everything else. Yashar Soltanzadeh led the technology, a senior technology and cybersecurity specialist whose work in secure, cloud-based enterprise platforms turned the approach into software.
Beneath the four, the build drew on many hands over the life of the product; the senior team owned the method, the analysis and the engineering.
Thierry Clarke
Tsvetelina Zapryanova
Jeremy Hunt
Yashar Soltanzadeh
