Agentic AI for the modern PE firm
From sourcing to exit, deal teams drown in CIMs, data rooms, and portfolio reporting. We deploy agents that do the first-pass work — inside your walls, with the lineage your LPs and the SEC expect.
- Deal sourcing & screening
- AI-assisted diligence
- Portfolio monitoring
- MNPI walls & audit lineage
The bottleneck isn't capital. It's reading time.
A mid-market fund screens hundreds of deals to close a handful, and every one buries the team in unstructured documents.
Associates spend their nights re-keying financials out of CIMs, reconciling QofE adjustments, and chasing data-room files that arrive in no particular order. Deal velocity is gated by how fast humans can read — not by conviction or dry powder.
Meanwhile the compliance surface keeps growing. Material non-public information has to stay walled between deals. Marketing and valuation claims fall under the SEC Marketing Rule. LP operational due diligence now probes your data handling and your AI use directly. Bolting a public chatbot onto a live data room is how a firm ends up explaining an MNPI leak to its GC.
Agentic automation fixes the throughput problem without creating a governance one — but only if the controls are built into the system, not promised in a policy doc.
Use cases across the deal lifecycle
Each agent is scoped to a fund or a single deal, acts through a logged action layer, and escalates judgment calls to the team.
Deal sourcing & screening
Agents triage inbound CIMs against your thesis, extract revenue, EBITDA, and growth, and rank opportunities so the team reads the right ten, not all hundred.
Diligence acceleration
Parallel agents index the data room, surface contract change-of-control clauses, flag customer concentration, and draft first-cut diligence findings for review.
IC memo drafting
Agents assemble the investment committee memo from diligence outputs, model summaries, and market comps — with every figure traceable to its source file.
Portfolio monitoring
Agents pull monthly reporting from portfolio companies, normalize KPIs, and flag covenant or budget variances before the board deck is due.
Value-creation tracking
Track 100-day plan initiatives across the portfolio, chase missing operator updates, and roll status into LP and GP reporting automatically.
Compliance & reporting
Generate Marketing Rule-compliant performance summaries and Form PF / LP report inputs with substantiation attached to every claim.
From a single workflow to fund-wide leverage
We prove value on one painful workflow before touching the next.
Scope
Pick one bottleneck — usually CIM triage or data-room indexing — and map exactly what the agent may read and do.
Wall
Stand up the perimeter and information barriers so the agent is bound to one deal and one fund, with no cross-contamination.
Deploy
Connect the data room, CRM, and fund systems through logged connectors, with human sign-off on every external output.
Expand
Once the team trusts the lineage, extend agents across sourcing, portfolio monitoring, and LP reporting.
MNPI containment by design
In PE, an information barrier isn't a courtesy — it's the line between a normal Tuesday and a wells notice. Generic AI tools treat access as a prompt instruction, which is no barrier at all. We treat it as architecture.
Every agent runs inside your VPC or on-prem, scoped to a fund and a deal at the connector level. Deal A's agent physically cannot resolve a reference to Deal B's data room. Nothing leaves your perimeter, nothing trains a public model, and every read and write lands in an immutable lineage record you can hand to an examiner.
- Per-deal, per-fund scoping enforced in the action layer
- No data egress to public model endpoints
- Immutable decision lineage for LP ODD and SEC exams
A public chatbot vs. an Automatic.co deal agent
Why a consumer AI tool has no business inside a live data room.
| A public chatbot | An Automatic.co deal agent | |
|---|---|---|
| Data location | Vendor's cloud, possibly used for training | Your VPC or on-prem, never trains a public model |
| MNPI walls | A line in the prompt | Enforced per-deal in the connector layer |
| Output | A draft you still have to verify | Findings with source files attached, queued for sign-off |
| Audit trail | A chat history, at best | Immutable lineage for every read, write, and review |
| Examiner answer | "We're not sure where it went" | Reproduce any finding or valuation input on demand |
Frequently asked questions
How do you keep MNPI and deal data from leaking between teams or models?
Agents run inside your perimeter, scoped to a single deal or fund by default. Information barriers are enforced in the action layer, not the prompt — a diligence agent on Deal A cannot read Deal B's data room, and nothing is sent to a public model endpoint or used for training.
Can agents touch the data room, the CRM, and the fund accounting system?
Yes. We integrate with DealCloud, Intralinks, Datasite, your CRM, and fund admin systems through governed connectors. Every read and write is logged, and high-stakes actions — sending an IC memo, updating a valuation — require human sign-off.
Will this stand up to an LP, auditor, or SEC examination?
That's the point. Every agent decision carries a lineage record: what it read, which model produced the output, who reviewed it, and when. You can reproduce any diligence finding or valuation input on demand, which is exactly what examiners and LP operational due diligence ask for.
We're a lean firm. Is this only for the mega-funds?
No. Lean deal teams get the most leverage — agents do the first-pass reads of CIMs, build the data-room index, and keep portfolio KPIs current so a six-person team covers ground that used to need fifteen. We start with one workflow and prove it before expanding.
Built for adjacent regulated workflows too
Pair PE deal automation with agents tuned for the functions around it.
Bring your worst diligence bottleneck.
One working session to map a single deal-team workflow to a walled, auditable agent — and the path to fund-wide leverage.