Background & Opportunity
Gain.pro’s story begins with a research process that looked normal inside investment and advisory firms but was structurally inefficient. Information about private companies existed in reports, filings, commercial databases, market studies and internal documents. Yet those sources did not automatically form a consistent working picture. Analysts had to identify the right company, map related entities, clean financial information, determine ownership, reconstruct transactions and compare market context before senior decision-makers could assess an opportunity. That work created two kinds of waste. The first was visible: researchers spent long periods collecting and formatting information rather than interpreting it. The second was less visible but strategically important: teams could reach different conclusions because their source sets, definitions and update routines differed. A company’s ownership might be recorded in one place, a financing event in another and a market description somewhere else. Without a common structure, knowledge remained scattered across individual workbooks and disconnected tools. Nicola Ebmeyer and Frister Haveman had encountered this pattern in their earlier work at McKinsey, Bridgepoint and Bain. They founded Gain.pro in the Netherlands in 2018 with the intention of making private-market research faster and more usable. The founding insight was not simply that more data was needed. Professional users needed data organised around the companies, people, investors, lenders, advisers and transactions that make an investment market intelligible. The early business had to discover how narrow the product should be and who would pay for it. The fact sheet records about a year and a half of product and sales exploration. That period is a reminder that a data business cannot rely on a large theoretical market alone. It must determine which work is painful enough to change, which information customers trust, how frequently the workflow repeats and whether the product can fit into existing decision processes. Within about six months of formation, Gain.pro obtained its first paying customers. This was an early signal that the problem was commercially relevant, but the company still needed to turn a promising information service into a repeatable B2B software proposition. A general company-information tool could be useful occasionally. A vertical platform had to become useful repeatedly, across screening, research, monitoring and transaction preparation. The product direction addressed that requirement through a combination of structured data and review. Gain.pro’s product materials describe AI and automated processes working together with analysts. The company also states that data can be traced to its source. This design is important because private-market information has a credibility problem. A figure without provenance may be fast to retrieve but difficult to defend in an investment committee or client conversation. A fully manual process may be trusted but difficult to scale. The combined model attempted to place automation on the collection and organisation side while preserving a human layer for verification and interpretation. The model should not be overstated. The verified materials do not establish that every item was independently checked by a person, nor do they provide a universal accuracy rate. What they do establish is a product design that deliberately combines automation with analyst review and source traceability. That choice helped the company address the central tension in data products: more coverage and speed can create more uncertainty unless users can inspect where information came from and how it was handled. Gain.pro progressively moved toward a structured data graph covering companies, investors, lenders, advisers and transactions. This structure changed the unit of value. Instead of presenting isolated documents, the platform could connect a company to ownership, historical transactions, sector information and relevant participants. Those relationships make discovery more useful because users can move from one known fact to adjacent companies, investors or deals. They also create a potential accumulation advantage: once relationships are structured and reviewed, future research may require less repeated mapping. The target users were professional teams with repeated, high-value research needs. The business model was B2B subscription SaaS for private-equity funds, M&A advisers, investment banks, consultancies and corporate clients. The platform supported company discovery, screening, research, tracking and transaction execution. This customer focus was strategically coherent. Such users already understand the cost of analyst time, the consequences of inconsistent information and the value of integrating research with the rest of a deal process. Customer evidence later reinforced that direction. Gain’s Parcom customer story says that Parcom used the platform for company analysis, market reports, financial data, transaction history and business-model assessment. It also states that Gain was integrated with DealCloud. The evidence does not provide a verified percentage reduction in research time or a quantified return on investment. Its value lies elsewhere: it shows the product being used for multiple research tasks and connected to a transaction-management environment. That is stronger workflow evidence than a claim based only on a product demonstration. The company’s positioning developed alongside the product. In 2023, Gain.pro completed a brand rework and website upgrade intended to communicate a high-end, modern and international identity. This was not disclosed as a revenue event, and it should not be treated as proof of customer growth. It did, however, support a clearer category narrative. A platform serving professional investors and advisers needed to appear dependable, focused and capable of operating across markets rather than as a local information experiment. International growth became visible in the company’s organisational footprint. Its 2025 culture booklet said that the team had grown from 5 people to 250 people over the previous five years. It described thousands of users and offices in Amsterdam, Frankfurt, London, Warsaw and Bangalore. The same official material referenced customers including KKR, Blackstone, Cinven, CVC, UBS, Lazard, Rothschild, McKinsey, Bain and BCG. These are meaningful indicators of reported reach, but they remain company-reported. The sources do not define thousands of users, disclose the paid share or active usage, or establish customer retention. This limitation matters because growth narratives can easily become financial claims. Public information supplied for this case does not disclose audited revenue, ARR, gross margin, profitability or retention. Media reporting mentioned approximately €10 million publicly disclosed in 2022 and approximately €20 million cumulatively later, but official materials did not provide financing rounds, investors, valuation or audit documentation. Those figures should therefore remain attributed media reports, not treated as verified financial outcomes. The February 2026 change from Gain.pro to Gain extended the company’s original thesis. Gain announced a “Private Markets Super App” positioning designed to bring sourcing, research, collaboration and execution into one platform. The strategic logic was that fragmented information tools can create fragmented workflows. If a team discovers a company in one system, researches it in another, collaborates elsewhere and tracks execution in a fourth, the organisation may reproduce the same duplication that existed in the original research problem. A single-platform ambition can reduce those handoffs, but it also raises the bar. A broader product must serve more jobs without weakening data quality or becoming difficult to use. It must also compete with specialised tools, internal research teams, public registries, commercial databases and adviser services. Gain’s defensibility therefore depends on more than interface design. It must continue building historical coverage, company relationships, review routines, source provenance and integrations that customers can use in daily work. The competitive environment confirms why the chosen model was demanding. PitchBook, SourceScrub and Grata were identified as competitors. Large funds and advisory firms may possess buyer power because they can combine several databases, maintain internal research capabilities or commission external work. Supplier power involves data sources, analysts and AI infrastructure. New entrants may use public data and modern AI to lower technical barriers, but they still face the challenge of creating a trusted historical graph and earning a place in sensitive workflows. Gain.pro’s answer was to combine capabilities that are individually imitable but collectively difficult to execute well. Automation can support scale. Analysts can support review. A relationship graph can support discovery. Source traceability can support confidence. Integrations can support recurring use. International teams can support coverage and service. None of these elements alone proves a durable advantage. Together, they form a coherent operating model around the needs of private-market professionals. The case therefore illustrates a sequence rather than a single breakthrough. First, the founders identified a repeated problem from their professional experience. Second, the company explored product and sales fit for about a year and a half. Third, it secured paying customers within about six months and focused on professional investment workflows. Fourth, it built structured profiles supported by automation, analysts and traceable sources. Fifth, customer adoption, international offices and reported team growth demonstrated reach. Finally, the company expanded the positioning from Gain.pro to Gain and from research software toward a Private Markets Super App. The strategic lesson is not that every data company should add more features or promise a super app. It is that platform expansion is more credible when it follows a trusted core workflow. Gain.pro began with a specific inefficiency: private-company research required too much manual collection and reconciliation. It created a product approach that addressed speed and trust together. The later platform vision was built on that base, not substituted for it. At the same time, the evidence requires restraint. The case supports product, customer, team and geographic progress; it does not support claims of audited financial success. For operators, that distinction is part of the lesson: build the workflow, prove adoption, preserve provenance and describe outcomes only as strongly as the evidence allows.
Original Problem / Model
The first strategic challenge was defining a problem important enough for professionals to pay to solve. Private-market data was already available from many sources, so simply adding another search interface would not create a strong position. Gain.pro focused instead on the work surrounding the data: company mapping, ownership identification, financial cleaning, transaction reconstruction and market comparison. That framing connected the product to a recurring workflow rather than an occasional information request. The first paying customers arrived within about six months, giving the company an early commercial signal while leaving the harder questions of repeatability, trust and scale to be solved.
Turning Point
The product model combined automation with analyst review. Automated collection and structured relationships could increase coverage and reduce repetitive work, while analysts helped address ambiguity and credibility concerns. Source traceability made it possible for users to inspect provenance. The verified evidence does not provide a universal accuracy rate or show that every item was independently checked, so the claim is about product design, not guaranteed perfection. This human-in-the-loop approach made the platform more suitable for investment and advisory teams whose decisions require defensible information.
Core Decision
Gain.pro targeted private-equity funds, M&A advisers, investment banks, consultancies and corporate teams. These customers repeatedly research companies and value connections between discovery, analysis and execution. The focus created a clearer use case than a general consumer information product and aligned the subscription model with professional workflows.
Execution / Model / Acquisition Changes
Customer adoption gave the model practical credibility. Gain’s Parcom story states that the customer used the platform for company analysis, market reports, financial data, transaction history and business-model assessment, and that Gain integrated with DealCloud. The source does not disclose a verified efficiency percentage, so the evidence should not be converted into a numerical productivity claim. Its importance is that the platform supported several connected research activities and fit beside a transaction system. That kind of integration can increase recurring use because data becomes part of an existing process rather than a separate destination. The company’s official materials also reported thousands of users and referenced major investment, banking and consulting organisations. These remain self-reported indicators; user definitions, paid proportions and retention were not disclosed.
Measurable Results
The 2023 rebrand and website upgrade helped Gain.pro communicate a premium, modern and international position. By 2025, official materials reported growth from 5 employees to 250, thousands of users and offices in Amsterdam, Frankfurt, London, Warsaw and Bangalore. Those figures show reported organisational and geographic expansion, but they do not prove revenue or profitability. In February 2026, the company announced the move from Gain.pro to Gain and introduced the Private Markets Super App concept. The new positioning extended the product from sourcing and research toward collaboration and execution, aiming to reduce the fragmentation created by multiple point tools.
Lessons
The case supports a disciplined conclusion. Gain.pro built from a specific manual-research problem, validated demand with early paying customers, combined automation with analyst review, and expanded through professional adoption and workflow integration. Its reported team, users and offices indicate reach, while the later Gain strategy shows platform ambition. However, public materials do not establish audited revenue, ARR, retention, margin or profit. The durable lesson is to earn platform expansion through a trusted core workflow, preserve source traceability and separate verified operating evidence from unverified financial inference.