Originally published in October 2025, this updated March 2026 edition reflects the market’s shift from AI experimentation to building the data infrastructure required to unlock value from Frontier AI models such as Claude. It introduces the Deal Engine framework and expands practical guidance for creating the proprietary context, institutional memory and integrated data foundations that allow private equity firms to apply these models effectively. Inside the guide Private equity is standing at the edge of its next competitive frontier, and our latest guide, Why every private equity firm needs a proprietary market data engine, explores how firms can get there. Drawing on data from the AI Pathfinder Private Equity Benchmark Survey (September 2025), the guide reveals that 47.8% of firms are still only piloting AI tools, while fewer than 11% have achieved true scale. Most have already invested in the plumbing. They have CRMs, market data platforms, and analytics tools. What they have not done is connect any of it into a single, context-driven intelligent system that learns and improves over time. That is the opportunity the guide unpacks. How leading firms are turning their datasets, CRMs, and proprietary knowledge into a unified data engine that shifts deal teams from reactive to proactive, spotting opportunities earlier, identifying relationship inroads, and building faster conviction. This updated guide distills insights from work with leading firms to give deal, portfolio, and data teams a practical playbook for what is next. Inside, you will find: What a PE data engine is, and what it is not How to buy, build, or "build with", and why "with" wins Best practices for deployment, adoption, and measuring success Real benchmarks from the AI Pathfinder Private Equity Benchmark Survey From reactive deal flow to proactive origination Too many deal teams are still waiting for deals to come to them. They respond to inbound flow, work from static target lists, and chase opportunities that are already in market. The firms pulling ahead have flipped that model. They are systematically hunting against defined criteria, picking up live signals, and reaching founders before a process ever begins. Making that shift requires more than better data subscriptions. It requires infrastructure. Signals around leadership changes, hiring spikes, new certifications, and facility expansions need to surface automatically and in context, rather than sitting buried across disconnected platforms. When those triggers are unified into a single operating layer, deal teams stop missing the moments that matter. From data abundance to deal advantage The industry's leading data providers have reshaped how dealmakers source and qualify opportunities. But with so many firms licensing the same high-quality datasets, access alone no longer creates differentiation. The next edge will come from how firms engineer, orchestrate, and apply intelligence to that data. A market data engine makes this possible by connecting internal systems and third-party sources, enriching them, and surfacing the most relevant opportunities in real time. As Phil Westcott, Founder and CEO of Deal Engine, puts it: "AI will not create advantage on its own. Proprietary, well-engineered context will. Firms that build a true data engine today are building the institutional memory and architecture that tomorrow's AI will depend on." The infrastructure gaps that cause firms to miss deals Most firms are closer to this than they think, but common gaps in their current setup are costing them deals. Fragmented relationship data. Key intel sitting in individual inboxes or spreadsheets rather than a unified system means lost visibility into founder and advisor relationships. Weak signal tracking. Without automated monitoring for hiring trends, leadership changes, or revenue inflections, firms miss companies that are quietly becoming ready to transact. Disconnected data and CRM. When market intelligence and relationship history live in separate tools, deal teams spend time reconciling systems instead of building conviction. No standardised workflow. Inconsistent processes across team members mean dropped follow-ups, duplicated outreach, and institutional knowledge that walks out the door.   A data engine closes these gaps. Not by adding more tools, but by connecting what already exists into a governed, compounding system.  The future is intelligent deal origination The AI Pathfinder data shows clear momentum. Nearly half of all firms are actively piloting AI, yet most are stalling because of architecture rather than ambition. Over half rate their data as adequate for pilots but not scalable. The firms that pull ahead will not be those with access to the best models. They will be those with the best proprietary intelligence behind them. The firms that act now will identify better deals, make faster decisions, and build lasting competitive advantage across the investment lifecycle. Download the guide to see how.

By Alex Bajdechi, VP of Sales, Deal Engine As private equity firms progress through 2026 and looking beyond, “data infrastructure” is becoming one of the most commonly used (and least consistently defined) terms in the industry. For some, it means better reporting. For others, it means a new CRM, a handful of third-party data feeds, or a dashboard layered on top of existing systems. In reality, modern data infrastructure is something far more comprehensive: a unified, intentional way of organizing, structuring, and activating institutional context and knowledge across the entire investment lifecycle. The expanding universe of data sources Today’s private equity firms are surrounded by data. Proprietary sources include internal documents, CRM records, relationship intelligence, expert network notes, portfolio insights, and years of institutional memory locked inside inboxes and spreadsheets. Third-party providers, such as Preqin, SourceScrub / Grata, Pitchbook, and Gain.pro - add structured market coverage and comparables. On top of that sits a constantly shifting layer of public data: web pages, press releases, filings, RSS feeds, hiring signals, product launches, and social activity. Each of these sources has value on its own. The challenge is that they rarely talk to one another, and context is often lost. Where most firms are today Most firms are still in the early stages of understanding and organizing their proprietary context and data. Some have taken meaningful steps, architecting purchased data feeds, deploying survivorship rules, or standardizing how information flows into their CRM with entity matching and orchestration. These are important foundations, and they shouldn’t be understated. But even among sophisticated firms, these efforts tend to exist in silos. Proprietary context lives in one place. Third-party data lives in several disparate locations. Public signals are monitored informally, if at all. The missing connective tissue Very few firms have fully threaded these data sources together, conceptually or technically. Fewer still have done so with a deliberate focus on architecture, growth, and workflow. That’s the difference between having data and having data infrastructure. True infrastructure ensures that information is continuously ingested, contextualized, de-duplicated, and made actionable across sourcing, diligence, and relationship management. It’s what allows firms to move faster without sacrificing judgment and navigate the ever changing AI landscape with confidence, and governance. Bringing the vision to life with Deal Engine This is where Deal Engine is uniquely positioned. We’ve productized what has historically been a painful, bespoke process, helping firms unify proprietary, third-party, and public data into a single, intelligent system designed around how modern deal teams actually work. The result isn’t just better data. It’s better decisions, better prioritization, and a platform that scales with the firm with trusted AI governance. Book a demo to learn how Deal Engine can help you embed a modern data infrastructure built for private equity in 2026 and beyond. Find out more about how Deal Engine helps dealmakers.

What private markets leaders are learning about AI, data, and the next phase of dealmaking Deal Engine was proud to sponsor Intapp Amplify this year, with members of our team attending both the New York and London events. Across the two conferences, Alex Bajdechi, Steven Kolatac, Phil Westcott, Martin Pomeroy, Sven Hansen and Matthew Kordonowy joined discussions with private markets and technology leaders and partners about how CRM, data infrastructure and AI are evolving across the industry. This team has worked closely with firms running Intapp DealCloud for many years. That perspective made the conversations across Amplify particularly interesting, as the industry begins to move from experimenting with AI tools towards building the infrastructure that allows AI to operate effectively. A major focus of the event was Intapp Celeste, Intapp’s new agentic AI layer designed to coordinate AI agents across the private markets workflow. Celeste enables firms to move beyond humans manually operating software interfaces and towards a model where professionals instruct AI agents to complete tasks across their technology stack. In practice, this means agents can interact with systems such as Intapp DealCloud to monitor signals, capture opportunities and automate operational processes.  In this best practice architecture, Deal Engine can provide the integrated market data to the DealCloud environment, the combination forming a firm-owned contextual layer to feed these agents. Across sessions and conversations with clients, partners and technology leaders, several consistent themes emerged.  1. AI advantage is shifting from models to context  The advantage lies in the proprietary market context that those models interrogate. For private markets firms, that context includes their (i) procured market data, (ii) their market context scraping, (iii) their proprietary notes and insight stored in their DealCloud, (iv) their proprietary sourcing history and rationale, (v) their evolving investment thesis and (vi) internal institutional knowledge in their Document Management systems Many conversations centered on how firms can better integrate and govern this context so that AI can interrogate and drive agentic workflow in a meaningful way.  2.Infrastructure thinking is replacing point tools Another recurring discussion was the move away from adding more standalone tools. Over time many firms have accumulated complex technology stacks, with different systems managing research, deal flow, market data and internal processes. While each tool serves a purpose, the overall result can be fragmented workflows and disconnected data. The focus is now shifting towards infrastructure. Firms are thinking more about how their systems and data connect together to form a coherent operating environment that AI can work across. 3. Investment strategy needs to be codified Several conversations focused on how investment strategy is represented inside technology systems. Historically, investment theses have often existed as narrative documents or presentations. Increasingly, firms are looking at how those strategies can be translated into the signals and intelligence so that the Frontier AI technology can interpret the market and surface opportunity. When investment criteria, sector focus and deal characteristics are “bottled” into the intelligence infrastructure, of a firm, the trained agents can guide sourcing workflows, highlight relevant opportunities and support AI driven analysis.  4. Data governance is becoming central to AI strategy Data governance was another major theme across the event. Reliable AI outputs depend on reliable inputs. As firms introduce more AI driven capabilities, questions around data structure, permissions and oversight become increasingly important. Rather than being treated purely as an operational issue, governance and data architecture are becoming core parts of long term AI readiness.  5. AI is supplementing relationship driven deal origination There was strong agreement that AI will not replace the relationship driven nature of private markets. Instead, the focus is on supplementing existing sourcing approaches and finding the right balance. Relationship networks and inbound opportunities remain essential, but firms are increasingly augmenting these with thesis driven intelligence that can surface opportunities earlier and more consistently. The goal is not to remove the human element of dealmaking, but to give deal teams better context, more leverage and prioritize where they are spending their time. 6. The shift from operating software to directing AI systems One of the more forward looking themes discussed was how professionals may interact with software in the future. Rather than manually operating multiple applications, the emerging model is one where professionals increasingly instruct AI systems to carry out tasks across those tools on their behalf. Technologies such as Intapp Celeste reflect this direction of travel, where AI can coordinate activity across different applications while keeping the human in control of the outcome. For private markets firms, this could gradually reshape how research, origination and internal workflows are managed.  The direction of travel for private markets technology Taken together, the conversations across the New York and London Amplify events pointed to a clear direction of travel. Frontier AI is advancing quickly, but the firms seeing the most value are those focusing on the foundations. Integrating data, structuring institutional knowledge and ensuring governance across systems are becoming critical steps in making AI useful in practice. For private markets firms, the most valuable assets remain their people and the knowledge they accumulate over years of investing. When that knowledge is coalesced within a well designed architecture, the organization stands to gain an exponential benefit in future years. Thank you to the Intapp team for hosting two fantastic market events! The conversations at Amplify made one thing clear: firms that structure and integrate their market intelligence today will be best positioned to unlock AI tomorrow. Speak with our team to learn more.