AI-Powered Intelligence for the World’s Leading Dealmakers

Gain is redEfining how prIvate equity and m&a teams discover, reSearch, and Act on market intelligence

When I joined Gain, the company was building an AI-native intelligence platform for private equity firms, M&A advisers, and enterprise strategy teams. Gain combined proprietary private-market data, AI-powered discovery, and deep workflow integrations into a single ecosystem used by some of the world’s largest investment and consulting firms. The platform served more than 400 enterprise clients and over 10,000 active dealmakers, and processed tens of thousands of AI-driven research queries daily.

My role centred on translating highly complex financial and operational datasets into scalable product experiences that enabled fast, high-confidence decision-making. Working closely with product, engineering, and data teams, I contributed across multiple areas of the platform, including AI-powered search, company intelligence pages, advanced filtering systems, benchmarking tools, workspaces, bookmarking flows, and collaborative research experiences.

A significant part of my work centred on visualising and integrating proprietary financial data and market intelligence. We designed interfaces that enabled users to benchmark thousands of companies against one another using metrics such as EBITDA, EBIT, free cash flow, invested capital, revenue growth, FTE growth, gross margins, valuation multiples, holding periods, and comparable transaction data. Through interactive bubble charts and scalable data visualisations, deal teams could identify patterns, opportunities, and market outliers in seconds.

Another core challenge was simplifying highly data-intensive workflows without compromising analytical depth. I improved information hierarchy, glanceability, search speed, and interaction patterns across the platform, helping users surface relevant insights more quickly while navigating large-scale datasets and interconnected company networks.

I also contributed to the evolution of Gain’s AI-assisted product layer by helping integrate machine learning and AI-generated intelligence into existing financial research workflows traditionally managed by large analyst teams. This work helped move the platform towards a more scalable and automated intelligence model, making data discovery, benchmarking, and market mapping significantly more efficient for global private market teams.

Investment-grade intelligence, interlinked & proprietary

The deepest private markets data graph, covering every company with >10 FTEs, 20,000+ investors, 500k+ deals, and 11,000+ advisors, all richly interconnected.

trusteD by >$1 trillion of private capital, 100% of mbB/Big-four and 80% top-20 m&a houses

reading Company value tHrough timE

Understanding a company’s valuation in a single moment only tells part of the story. To give investors and deal teams deeper historical context, I helped design a valuation analysis experience that exposed how company value evolved over time across both public and private markets.

The feature combined historical market performance, valuation multiples, and financial ratio analysis into a single research workflow. Users could analyse trends across metrics such as EV/EBITDA, EV/EBIT, EV/Sales, invested capital, and free cash flow while comparing historical valuation shifts against broader market movements and business performance.

One of the key challenges was creating a system that worked seamlessly across listed and non-listed companies. Public businesses offered continuous market pricing data, while private companies required intelligent modelling based on comparable transactions, benchmark groups, and proprietary financial datasets. Working closely with engineering and data teams, we designed interfaces that unified these different valuation models into a consistent and understandable product experience.

The result allowed users to move beyond static company snapshots and instead understand valuation as a dynamic story shaped by growth, profitability, market conditions, and transaction activity over time. Through interactive charts, historical ratios, and contextual benchmarks, analysts and deal teams could identify valuation patterns, market shifts, and pricing anomalies significantly faster than through traditional research workflows.

frOm Comparable deals to confideNt valUations

Understanding what a company is worth often starts with understanding what similar businesses were actually acquired for. To make this process faster and more accessible, I helped design and develop the “Precedent Transactions” layer within Gain’s valuation platform.

The feature surfaces relevant historical transactions directly inside company profiles, allowing users to benchmark businesses against comparable deals using metrics such as Enterprise Value, EV/EBITDA, EV/EBIT, EV/Sales, revenue, FTEs, deal timing, and transaction type. One of the biggest challenges in private markets is that reliable valuation data is fragmented, difficult to discover, and often buried across disconnected sources. Working closely with product, engineering, and data teams, we designed a system that transformed large-scale transaction datasets into a searchable and highly contextual valuation workflow.

Transactions were ranked dynamically based on company similarity, recency, deal structure, and available financial metrics, helping analysts and deal teams surface relevant benchmarks in seconds rather than days. Advanced filtering and comparison tools allowed users to explore valuation ranges across industries, growth profiles, and transaction types with far greater speed and confidence. The result was a major step forward in Gain’s mission to reduce friction in private market research workflows, moving from saving users their first day of work to potentially saving entire weeks of manual analysis and benchmarking.

tuRning private market datA Into compeTitive positIoning

One of the most powerful challenges in private market research is understanding how companies truly compare against one another beyond simple revenue rankings or industry labels. To solve this, I helped design and develop Gain’s benchmarking experience, allowing deal teams and analysts to map companies against complex operational, financial, and strategic metrics through interactive visualisation models.

The feature transformed large-scale company datasets into dynamic benchmarking maps that let users compare businesses across dimensions such as revenue growth, FTE growth, EBITDA margins, ownership structures, market maturity, profitability, and strategic positioning. Through interactive bubble charts and ranking systems, users could immediately identify clusters, outliers, growth trajectories, and comparable businesses across thousands of companies.

A key part of the challenge was balancing analytical depth with usability. The platform handled highly data-dense workflows, yet the experience needed to remain fast, intuitive, and readable at a glance. Working closely with engineering and data teams, I contributed to interaction patterns, filtering systems, visual hierarchy, and scalable charting solutions that made advanced market mapping accessible across both research and deal-execution workflows.

The benchmarking system became a powerful discovery layer within the platform, helping users move beyond static company profiles toward a more contextual understanding of market positioning, competitive landscapes, and investment opportunities.

By combining AI-powered intelligence with visual analysis, the feature significantly reduced the time required to identify comparable businesses and uncover strategic patterns across private markets.