Thesis-driven sourcing agents
Sourcing coverage is capped by analyst hours. Parallel agents each search one sector for private companies that match the investment thesis.
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Private Equity & Venture Capital
Your team already knows AI matters. We help you turn that into operational advantage across sourcing, diligence, portfolio operations, and fund workflows.
Teams we've trained include
- A European PE firm with €80bn in committed capital across technology, consumer, and healthcare
- A European seed and Series A VC firm founded by technology entrepreneurs and company builders
- A European mid-market PE firm specialising in buyouts across healthcare and digital infrastructure
- A partner-led mid-market UK PE firm with investment teams across Manchester, London, and Birmingham
- A UK venture capital firm investing in early-stage companies across the food value chain and public health
- A global impact investment platform with $5.5bn AUM, backed by one of Europe’s largest family offices
- A mid-market PE firm focused on situational buyout opportunities across the UK, Ireland, and Nordics
- A US middle-market PE firm using a data-driven build-and-buy approach in essential services
- A UK lower mid-market PE firm investing in owner-managed businesses across services and technology
- A UK growth equity firm backing high-margin, IP-led SMEs across services, industrials, and technology
- A UK investment group spanning venture, growth equity, and infrastructure with £850m+ under management
- A family office managing a global multi-asset portfolio spanning property development and growth capital
Why firms like yours hire us
- Our AI training for private equity and venture capital firms turns individual experiments with ChatGPT, Claude or Copilot into firm-wide capability across the deal cycle.
- Sessions use your own IC papers, board packs, portfolio reporting, DDQs, LP materials and internal approvals.
- PE and VC is our deepest sector, with clients across buyout, growth, venture, impact and infrastructure strategies.
- We train each team individually, and one-to-one coaching is available for anyone who wants help on their own work.
AI use cases from our private equity and venture capital clients
Each one was built in our sessions with clients like yours, and comes from our library of more than 1,000 AI use cases.
Deal screening against your criteria
Inbound decks pile up. Screening agents score each one against the firm's investment criteria and draft the first questions for founders.
Board pack change detection
Restated numbers slip through. Month-to-month comparisons flag changed figures and metric definitions, with page citations.
Memos tested by a simulated IC
IC prep eats the week before the meeting. First-cut memos are grounded in the deal files, then challenged by a simulated investment committee.
Diligence data that reconciles
Data room figures rarely tie out first time. Cohort dashboards run coded validation rules that flag numbers that do not reconcile.
Live sector market maps
Market maps go stale quickly. Maps refresh weekly from earnings calls, patent filings, hiring patterns and regulatory change.
The numbers your competitors are watching
Four data points showing how fast the PE and VC landscape is moving on AI. Each links to its primary source.
How your peers are moving
AI adoption across private equity and venture capital is accelerating fast. From AI-powered deal sourcing and due diligence to portfolio value creation and fund operations, here's how some of the largest PE and VC firms are already putting AI to work.
We can usually identify your highest-impact use cases in a single conversation.
What we train on
- Sector mapping, off-market target identification, and origination pipeline enrichment from fragmented signals
- Faster, more structured summarisation of CIMs, investment memos, board packs, and proposal documents
- Stress-testing investment theses, spotting red flags, and supporting first-pass due diligence
- Portfolio-company performance tracking, margin signals, and cross-board-pack trend analysis
- DDQs, fundraising materials, investor updates, and LP communications with more consistency and less manual work
- Better IC prep through sharper synthesis, challenge, and follow-through
- Custom research agents trained on your firm's own sector knowledge, deal history, and internal frameworks
What makes us different
- Everything is bespoke. Each session is built around your own data, workflows and use cases, such as your IC papers, board packs, DDQs and portfolio reporting, so people practise on real work.
- Our instructors are AI practitioners, not career trainers. They are founders, operators and advisers who use AI in their own work every day, so they answer the hard questions from direct experience.
- We take a data-driven approach. We survey your teams before and after the programme and measure time saved, AI adoption and return on investment, so you can see exactly what changed.
- We are a full-service AI enablement partner. Beyond the training, we offer AI strategy for leadership, one-to-one coaching for key people, and rollouts across multiple teams.
How we get your team there
A practical playbook
- 1
Anchor it in the investment thesis
Start with the parts of the lifecycle where faster synthesis, better coverage, or stronger margin discipline will create the most value. Firms that treat AI as part of the investment thesis (rather than a horizontal productivity tool) build capability that compounds across deals and funds.
- 2
Build on real documents and workflows
Teams adopt faster when training uses their actual IC papers, board packs, DDQs, portfolio reports, and internal templates. Abstract demos generate interest. Working with live artefacts changes behaviour.
- 3
Fix the data foundation early
A cleaner single source of truth across portfolio data, CRM, ERP, and reporting systems is what turns AI from a clever assistant into a usable operating layer. Most firms find that data quality and access controls, not model capability, are the real constraints.
- 4
Measure adoption, not enthusiasm
Track time saved, workflow quality, and use-case adoption rates so AI is judged by operating impact, not seat count or pilot excitement. The goal is a smaller set of workflows that are measurably better and that the team trusts enough to rely on.
“Having conversations, using my tone of voice, creating research documents and so on. All of it was completely new. Loved it.”
Participant at a London private equity firm
“It was structured really well with breaks and building on concepts without feeling too overwhelming.”
Participant at a mid-market PE firm
“Brilliant. We are a hard group to keep on track.”
Participant at a venture capital and advisory firm
Ready to build AI capability in your firm?
We can map the highest-value use cases for your team and design bespoke sessions around your actual documents and workflows.
Sources and further reading
OECD: Venture capital investments in AI through 2025AEA: Machine learning prediction of PE fund performanceSSRN: Artificial intelligence in venture capital operationsSSRN: Venture capital (mis)allocation in the age of AIMIT Sloan Review: Building AI capabilities at ApolloFinancial Times: How PE's big bet on software was derailed by AIFinancial Times: Vista to cut staff in favour of AIThe Economist: Private equity barons have a giant AI problemThe Economist: What if the $3trn AI investment boom goes wrongNew York Times: AI companies set fund-raising recordsThe Guardian: Global datacentre boom, investment, and debt