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AI for VCs and private equity firms.

Build deal-flow systems, automate diligence prep, and deploy portfolio-company AI playbooks. We are the implementation layer between your thesis and the workflow.

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AU-region by default Mutual NDA in 24h Portfolio playbook included

What does AI implementation look like for a VC or PE firm?

Two layers. Layer one is the firm itself: deal-flow capture, diligence prep, IC memos, LP reporting. Layer two is the portfolio: a shared AI playbook so every company gets the same foundation. We build both layers, then stay on as your fractional AI ops lead across the fund.

The numbers that move the conversation

78%
of VC partners use generative AI weekly

According to the SVB 2025 State of VC report. Adoption is no longer the question — orchestration is.

$150K
avg. annual cost of an internal AI lead

Per Glassdoor AU 2026, loaded cost lands closer to $250K. A fractional model gets you operational in week one for a fraction of that.

40%
faster diligence prep with structured AI

Bain & Co. private-equity AI survey 2025: top-quartile funds cut first-pass diligence from 3 weeks to under 2 with a structured playbook.

AI consultant vs in-house AI hire vs doing nothing

The three real options. We are direct because the comparison matters.

DimensionAI consultant (THL)In-house AI hireNothing
Year-one cost$60K–$180K flat-fee$250K+ loaded$0 (apparent)
Speed to first deployWeek 1Month 4–6Never
Ongoing riskWe rotate out cleanlySingle point of failureFalling behind every quarter
Knowledge retentionDocumented playbook staysWalks out the doorNone to retain
Vendor lock-inArchitecture survives swapsOften vendor-shapedN/A

How a VC or PE engagement runs

Discovery
Week 1

NDA signed. We map deal-flow, diligence, IC and LP-reporting workflows. Audit your stack (CRM, doc room, comms).

Firm-layer build
Weeks 2–4

Deal-flow capture into Attio or HubSpot. Diligence assistant trained on your thesis. IC memo drafter with your house style.

Portfolio playbook
Weeks 4–6

One shared AI playbook deployed to each portfolio company. Same foundation, configured per business.

Fractional ops
Ongoing

We stay on as fractional AI ops across the fund. Quarterly playbook updates as models evolve.

What an engagement looks like

Hypothetical — pattern based on our talent-agency and SMB deployments. (No client identifying details: we treat fund-level work as confidential by default.)

EXAMPLE A $200M AUM Sydney growth fund — 8 partners, 14 portfolio companies
Firm layer
Deal-flow agent
Pulls signals from Crunchbase, LinkedIn, Twitter/X via Tavily and Firecrawl. Drops scored leads into Attio. Partners get a ranked Monday digest.
Diligence pack drafter
One Claude project per deal: market sizing, competitor map, founder background, risk register. First draft in <2 hours instead of 2 days.
IC memo + LP-update generators
House-style templates with structured inputs. Partners edit, never start from blank.
Portfolio layer
The shared playbook
Claude Teams or ChatGPT Enterprise, Attio CRM, n8n automation, Keeper for secrets. Every portfolio company gets the same stack with vertical-specific prompt libraries.
4-week portfolio sprint
One company at a time. We map their workflows, ship 1–2 high-leverage automations, train the team. Fund subsidises the first; portfolio company keeps paying after.

Book the 30-min discovery call →

Common questions from GPs and operating partners

What does AI implementation look like for a VC or PE firm?

Two layers. Layer one is the firm itself — deal-flow capture, diligence prep, IC memos, LP reporting. Layer two is the portfolio — a shared AI playbook so every portfolio company gets the same foundation. We build both layers, then stay on as fractional AI ops across the fund.

Do you sign NDAs and work inside our tenant?

Yes to both. Mutual NDA before we see a single deck. We build inside your Google Workspace, your Notion, your HubSpot, your Slack — not a separate tenant we control. Portfolio engagements mirror the same pattern: their data, their stack.

How is this different from hiring an internal AI lead?

An internal AI lead costs $200K–$350K loaded and takes 4–6 months to hire. We are operational in week one, work across every portfolio company without re-onboarding, and rotate out cleanly the moment you have an internal hire ready to take over. Most VC clients keep us at 0.4–0.6 FTE-equivalent, billed flat-fee.

Will you work with our portfolio companies directly?

Yes — usually where the leverage is. We deploy the same playbook across multiple portfolio companies so each gets a 4-week AI sprint at a portfolio-discount rate. Fund typically subsidises the first; portfolio company carries the retainer from there.

What data residency rules do you follow?

AU-region by default (Acronis, Google Workspace AU, AWS Sydney). For PE firms with US LPs, we mirror to a US region. We never train external models on portfolio data. Every system is built so a vendor swap is a config change, not a rebuild — the architecture survives a provider going dark.

Adjacent reading

AI Readiness Assessment

10 questions, 3 minutes, instant results. Use it with portfolio CEOs before a kickoff call.

Take the assessment →

State of AI Readiness: Australian SMB 2026

First-party survey of 54 Australian SMBs. The base rate for any portfolio company you back here.

Download the report →

AI for legal firms

Privilege-aware AI — relevant for fund-counsel work and LP-side compliance.

AI for legal →

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