AI in private equity for deal sourcing, due diligence and portfolio monitoring
We build AI agents that screen deals against your mandate, read the data room with a page citation for every finding, and check each portfolio company's KPI pack the day it lands. They run in your own cloud, and a deal lead approves every finding before it reaches your investment committee.
Jason, our COO, runs the call and scopes the first workflow with you.
- Data room indexedFiles sorted into financial, legal, commercial and HR workstreamsDone
- Customer contracts readTerm, pricing, exclusivity and change of control, per contractDone
- Revenue tied outQuality of earnings figures checked against monthly management accountsDone
- Deal team reviewTwo customers can terminate on a change of control2 clauses
- Red-flag memo draftedEvery finding cites the file and page it came fromDraft
- Deal lead sign-offApproved findings move into the investment memoApproval
What the agent can seeThis deal's data room only. Nothing else on your network is in scope.
- Contracts and schedules
- Visible
- Financial statements and QoE report
- Visible
- Management presentation
- Visible
- Other deals' data rooms
- Not retrieved
- Fund and LP records
- Not retrieved
- Model training on your files
- Never
Illustrative run. Workstreams, checks and reviewers are set per deal during scoping.
Clients include
Private equity value creation now carries more of the return
held in 32,000 unsold portfolio companies
Source: Bain & Company, Global Private Equity Report 2026Longer holds put more of the return on what you change inside each company. Bain's 2026 report says performance will rely on a firm's ability to generate strong EBITDA growth at its portfolio companies.
Almost 40% of companies are held for more than five years
That share was 29% in 2019, and the average hold at exit now sits around seven years. An agent built in year two of a hold has years to pay back.
Nearly 20% of portfolio companies run generative AI in production
Most use cases are still pilots. We build against the company's real systems and data from the first week, so the pilot is the production build.
Diligence and monitoring still run on documents
Data rooms, KPI packs and board decks take the analyst hours. Each is a document workflow with written rules and a person who reviews the output, which is where agents earn their keep first.
Your partners keep the judgment
Agents read, extract, compare and draft. Partners decide what to buy, what to pay and what to change, and the AI Audit ranks which workflow to build first.
Sources: Bain & Company, Global Private Equity Report 2026 (Private Equity Outlook 2026: Gaining Traction); Bain & Company, Global Private Equity Report 2025 (survey of investors with $3.2 trillion in assets under management, September 2024).
AI agents for private equity, from first screen to exit
Six workflows where your deal team, your operating partners or a portfolio company get analyst hours back, each with a person approving what the agent produces.
Deal sourcing against your mandate
Agents read inbound teasers and the public sources you choose, such as company websites, registries and news, score each target against your sector, size and geography criteria, and log it in your deal CRM.
Built on self-hosted n8nReactivated deal logs
Agents re-read the deals that never reached an LOI, flag the ones that fit your current fund's criteria, and attach the reason each was passed on the first time.
How agents read your past deal notesData room review
Agents index the data room, extract terms from contracts and leases, tie the financials to source schedules, and draft a red-flag list with a page citation for every item.
Same document pipeline as our invoice automationInvestment memo first drafts
Agents assemble the approved findings, your model outputs and the market notes into your memo template, so the deal team edits a full draft before the investment committee meets.
How approval gates workPortfolio KPI monitoring
Agents read each company's monthly pack the day it lands, test every KPI against budget and its own expected range, and alert the operating partner when one moves.
See a portfolio KPI checkWorkflows inside portfolio companies
Content, customer service, finance and order workflows built inside a portfolio company's own systems and run after launch by the engineers who built them.
See AI managed servicesHow a private equity AI build runs
The same team carries your workflow from the first call to the system running in production. If you want proof first, an AI pilot builds one workflow on your data in 2 to 6 weeks for $35,000 to $75,000, and its fee is credited toward the build. See how we work
Jason, COOAI Audit, if you need one
Eight phases across strategy, operations, data and systems over 4 to 8 weeks, ending in a ranked 90-day roadmap with ROI models for the top three to five initiatives. It costs $10,000 to $100,000, credited toward your first build.
- Jason, COO
Scope
One workflow, the systems it touches and what it will take, agreed before you commit. Owen joins the first calls until you are onboarded.
- The documents and systems each agent may read
- The findings that always go to a person
- The timeline, before you sign
Maciej, CTOBuild
Built against your real data, in your environment, as production software from the first week.
- Staging first, on a closed deal's data room or past KPI packs
- Your deal team tests it on real files before go-live
- Self-hosted n8n in your cloud
- Jason, COO
Integrate
Into production behind your auth, your governance and your monitoring, with your team trained on it.
- Runs behind your own auth
- Every run logged with what it read and who approved it
- Your people trained on the system
- Maciej, CTO
Run and improve
Watched, fixed and extended in weekly sprints by the engineers who built it.
- Error alerts to the engineers who built it
- Weekly sprints that you direct
- The next workflow or portfolio company, scoped with you
What your firm gets from a build with us
We build the agents around your mandate and your process, and run them in your environment.
| What you get | |
|---|---|
| You receive | Working agents inside your deal CRM, data rooms and reporting |
| Where deal data lives | In your cloud, on self-hosted infrastructure |
| Fit to your process | Built around your mandate, diligence checklist and KPI definitions |
| Who approves | A named person on every finding that reaches the committee or a board |
| Timeline | Scoped per workflow and agreed before you sign |
| After launch | The engineers who built it monitor and extend it |
What private equity firms ask about AI
Ask us something elseHow is AI used in private equity?
The workflows we build cover four places: screening deals against a mandate, reviewing data room documents in diligence, monitoring portfolio KPI packs, and running workflows inside portfolio companies. Bain & Company found in its 2025 Global Private Equity Report that nearly 20% of portfolio companies had generative AI use cases in production with concrete results, while most were still in pilots. Each of the four is document work with written rules and a person who reviews the output.
What is agentic AI in private equity?
An agent takes a goal, such as reviewing 40 customer contracts for change-of-control terms, and works through the steps itself: it opens each file, extracts the clause, checks it and escalates what it cannot resolve. In our builds, agents run on self-hosted n8n, log every step, and stop at an approval gate before anything reaches your investment memo or a portfolio board.
How does AI help with due diligence?
It takes the first read of the data room off your analysts. Agents extract contract terms, tie the financials to source schedules, list missing documents and draft red flags, each with the file and page it came from. A second agent re-checks every finding against its source, and your deal team reviews findings instead of hunting for them.
What is portfolio monitoring in private equity?
Tracking each portfolio company's KPIs against budget, covenants and the value creation plan through the hold, usually from monthly packs in each company's own format. An agent reads every pack, tests every KPI and alerts the operating partner when one moves.
Is AI going to take over private equity?
No. Agents read, extract, compare and draft, and partners decide what to buy, what to pay and what to change. Every build we ship puts a named person on the approval gate for anything that reaches the investment committee, an LP or a portfolio board.
Does deal data leave our environment?
No. We build on self-hosted n8n in your own cloud, behind your own auth, and each deal runs in its own workspace. A model call carries only the passages its task needs and runs under your own agreement with the model provider, or goes to a model you host. We never use your files to train a model.
Where should a private equity firm start with AI?
With the workflow where your team spends the most hours on documents with clear rules, usually deal screening or the first pass of diligence. If you know which one, book a discovery call and Jason will scope it. If you have several candidates across the firm and the portfolio, the AI Audit ranks them in 4 to 8 weeks for $10,000 to $100,000, and its fee is credited toward your first build.
Industries on the other seats at your deal table
Venture firms, the banks that run your processes, accountants and consultants read the same companies and the same numbers from a different side.
AI for venture capital
Inbound deck screening, diligence file checks, founder updates and LP letter drafts, with a partner approving every message.
See AI for venture capitalAI in investment banking
Comps spreads, CIM section drafts and model checks for the banks on your deals, with every figure traced to its source page.
See AI in investment bankingPut your first workflow on the board
Answer a few questions about your team and what you want automated, then Jason scopes it with you on the call.
