~95%
Pilot purgatory
Of AI pilots never reach production. Silos, duplicated cost, no shared playbook across the portfolio.
Industry benchmark
AI implementation — private equity
Sharper at the deal table. Stronger in the portfolio.
We build AI into both sides of private equity: Fund Intelligence — faster sourcing, diligence and monitoring at the fund — and Value Creation — higher EBITDA inside the portfolio. Built by an operator who's sat in the deal seat and ships production AI. One workflow, in production, in 90 days — measured. No pilot purgatory.
Built by a PE operator who ships production AI
↳ $350M+ deployed1.53× net MOICStanford GSB
Portfolio intelligence
Diligence cycle
−78%
300→66 hrs
EBITDA impact
+$1.6M
annualized
Alerts
3
2 portcos
Cumulative value captured+$4.2M YTD
!Northwind Logistics — churn velocity +12%flagged 3 weeks before the board pack · intervention queued
updated 2m agovendor-neutral · in-tenant
Due-diligence accelerationDeal-sourcing signalPortfolio monitoringReporting automationBack-office opex
Due-diligence accelerationDeal-sourcing signalPortfolio monitoringReporting automationBack-office opex
The problem
You don't have an AI-idea shortage. You have an execution gap.
The cost of the status quo is measured in analyst hours, decision lag, and EBITDA you can't show at exit. Here's what it's costing — in numbers.
40–60hrs
Diligence drag
Analyst hours per deal on document review — plus ~300 attorney hours on legal DD and 3–5 days of IC lag.
Industry benchmarkQuarterly
Blind monitoring
Board packs assembled by hand. Problems surface after the quarter closes — late intervention, eroded value.
Voice of customer1 CFO, N portcos
Reporting load
The fund CFO is the de facto finance department for half the portfolio. Manual consolidation breaks as you scale.
Voice of customerWe have no shortage of AI pilots. What we lack is a clear path to measurable, repeatable value.
— Portfolio operating partner, paraphrased from PE value-gap research. The recurring question we hear: "Where does AI actually hit the P&L?"
Practice 01 · Fund Intelligence
Your deal engine, at machine speed.
AI built into the fund's own workflows — sourcing, diligence, portfolio monitoring and LP reporting — so the team covers more ground, reaches conviction faster, and spends its hours on judgment, not grunt work. Measured in hours saved, deals covered, and cycle time.
01 — Due-diligence acceleration
Read the data room in an afternoon, not a fortnight.
Automated extraction and review across contracts, CIMs and financials — structured signal, flagged risks, model-ready figures — with a human check before IC.
Deployed: a diligence assistant wired to your data-room, with audit trailBefore
40–60h
doc review / deal
After
−78%
review time
+3–5 days faster IC decision · third-party DD spend cut
02 — Deal-sourcing signal
A top-of-funnel that works while the team sleeps.
Continuous monitoring of hiring, web-traffic and filing signals surfaces off-market targets and pre-scores them against your thesis — so associates screen, not scrape.
Deployed: a scored, deduped sourcing pipeline feeding your CRMBefore
Weeks
to screen a cohort
After
+33%
more companies, in minutes
43% of flow sourced via AI at top-quartile firms
03 — Portfolio monitoring & early warning
See the miss weeks before the quarter closes.
Leading-indicator alerts on pipeline velocity, churn and retention — plus automated peer benchmarking — flag drift early enough to act, not just explain.
Deployed: a live portfolio dashboard with threshold-based alertsBefore
Post-close
surprises
After
Weeks
of early warning
Earlier intervention protects value that late board packs already lost
04 — Reporting automation
Stop being the finance department for the whole portfolio.
Automated consolidation and report generation across portcos — LP packs, board decks, KPI roll-ups — so the fund CFO supervises a process instead of stitching spreadsheets.
Deployed: a reporting pipeline that consolidates across entities on a scheduleBefore
Manual
spreadsheet stitching
After
Automated
consolidation
Reconciliation drag and reporting risk fall as you scale, not rise
Before/after figures are industry benchmarks for these fund workflows (2025–26); your audit sizes the equivalent on your own team.
Practice 02 · Portfolio Value Creation
Turn AI into EBITDA — inside the companies you own.
AI built into portfolio-company operations — finance and back office, customer operations, and commercial — to take cost out and lift margin. Higher EBITDA now; a stronger multiple at exit. Built once in one portco, then templated across the portfolio.
01 — Finance & back-office automation
Take cost out of the back office.
AI into finance, AP/AR, month-end close, onboarding and internal support inside the portco — the opex that quietly suppresses EBITDA and the exit multiple. Built once, templated across the portfolio.
Deployed: production automations inside the portco, with a reusable playbookBefore
Opex drag
on EBITDA
After
+18%
productivity lift
50% faster onboarding · documented $1.65M annualized portco gains
02 — Customer operations
Serve more customers without adding headcount.
AI triage, drafting and deflection across support and success — faster resolutions and a lower cost-to-serve, with satisfaction that holds as volume grows.
Deployed: an assisted support pipeline with human review on the edge casesBefore
Manual
every ticket
After
−60%
time per ticket
Lower cost-to-serve as deflection and faster resolution scale — while NPS holds
03 — Commercial & revenue
Find the margin hiding in pricing and pipeline.
AI for pricing, lead scoring and sales-ops — surface under-priced segments and high-intent demand so the commercial team compounds revenue, not just trims cost.
Deployed: a pricing and lead-scoring model wired into the CRMBefore
Gut-feel
pricing & targeting
After
Data-led
revenue lift
Margin from revenue — the other half of EBITDA, not just cost-out
Portfolio figures are industry benchmarks for AI-driven value creation (2025–26); your audit sizes the EBITDA opportunity in a target portfolio company.
How it works
Audit → map to the P&L → build & deploy → measure & scale.
The audit is the low-friction on-ramp, not the product. The product is a workflow in production with a number attached to it.
Step 1 · On-ramp
AI Efficiency Audit
We map your workflows and rank the 3–5 where AI hits opex/EBITDA fastest — each sized in hours, dollars and cycle time. Free.
5 business daysStep 2 · Diagnose
Map to the P&L
A value map tied to your financials, a vendor-neutral platform call, and a 90-day roadmap with an ROI model you can take to IC.
2 weeksStep 3 · Build
Build & deploy
We ship one high-impact workflow into production — integrated, governed, with your team enabled to run it. A system, not a slide.
4–8 weeksStep 4 · Prove & scale
Measure & scale
Before/after measured against the day-one baseline. Then the same playbook runs company by company across the portfolio.
OngoingWe don't bolt on a tool — we build into the operation.
Integrated into your stack, governed to your infosec standard, and measured against the baseline we capture on day one.
Get the free audit → Why Venture Core
The alternatives advise, sell a box, or take nine months. We ship.
Nobody owns execution at portfolio scale. That's the gap — and the only one we work in.
| Venture Core AI | Big-4 / strategy | Point-tool vendor | Internal AI hire | |
|---|---|---|---|---|
| Actually builds & deploys | Ships to production | ✕ deck, then leaves | one box only | eventually, alone |
| Operating / P&L context | PE operator-led | strategy, not ops | ✕ no PE context | varies |
| Measured before/after | Guaranteed or no build fee | ✕ | ✕ | rarely formalized |
| Time to first measured impact | ~90 days | months, no build | fast install, no outcome | ~9 months to hire |
| Cost shape | Fixed-scope, from $25k | six-figure deck | per-seat, ongoing | $250k+ / yr |
| Scales across the portfolio | Repeatable playbook | re-engaged each time | tool sprawl | ✕ single-threaded |
Proof
Owned, not borrowed. Honest, not inflated.
We're pre-client, so we don't invent logos or results. Credibility comes from a real track record, a visible method, and a guarantee we actually carry.
The operator's track record
- Built a co-investment book $0 → $350M+ at 1.53× net MOIC as Principal at Villarica Partners.
- ~8 years across private equity, investment banking and a ~$4B family office.
- LP-side relationships with Apollo, KKR and CD&R; Stanford GSB MBA '24.
- A founder who ships production AI — an AI SaaS and a DTC brand, both live.
The size of the prize
50–78%
DD-review time cut
95%
Pilots that stall pre-production
70%
Of AI value is process & governance
53%
LPs rank AI value-creation top-5 in GP selection
Industry benchmarks (2025–26) — labeled as such, never dressed up as Venture Core results.
The one before/after that is ours
First-party · founder's own productBefore we install AI in your portfolio, we ran it in our own. The founder ships production AI inside Ezora Health — a live DTC brand. Here's the same kind of before→after we'll measure for you, on a workflow we own end-to-end: the customer-service and lifecycle-email pipeline.
−70%
Time per customer-service reply after an LLM triage + draft layer shipped to production
3→1
Days to ship a full lifecycle email flow — research, copy, build — now one working session
Live
In production today, run by the team, not a slide. We'll walk you through the build on a call
The founder's own shipped work — not a client result and not a benchmark. Evidence that the person scoping your build has put production AI into a real P&L. Verifiable at ezorahealth.com.
Our conviction, on paper.
The audit is free. And the build carries a build-fee risk reversal: measured improvement, or you don't pay the build fee. The success metric and baseline are agreed in writing before we start — so "improvement" isn't ours to grade. We only win when the number moves.
One workflow, in production, with a number attached.
Not a model demo. Not a dashboard you'll never open. A measured operating improvement that compounds across the portfolio.
Start with the free audit → Pricing
Transparent, fixed-scope, and risk-reversed.
Start free. Pay only at the step where you've already seen the value. Every price is on the page — no "contact us for pricing."
AI Efficiency Audit
For one portfolio company — or the fund.
$0$2,500
- A prioritized AI Efficiency Map
- The 3–5 workflows where AI hits opex/EBITDA fastest
- Each sized in hours / $ / cycle time
- Risk-reversed — yours to keep
Diagnostic Sprint
For funds ready to commit to a plan.
$7,500fixed
- Deep value map tied to the P&L
- Vendor-neutral platform decision
- 90-day implementation roadmap
- An ROI model you can take to IC
Implementation Program
For funds that want it built and live.
$25kfrom, fixed-scope
- One high-impact workflow built & deployed
- Measured before/after vs. day-one baseline
- Team enablement to run it without us
- Build-fee guarantee: improvement, or you don't pay it
Tier 4 · Expand
Fractional AI Operating Partner
A repeatable cross-portfolio playbook — audit → deploy → measure → scale, company by company. The same engine that proved out on one workflow, run across the portfolio on a retainer.
Retainer
Portfolio-wide · ongoing. Scoped to your number of portcos and cadence.
A full-time AI lead is $250k+ and nine months to hire. A Big-4 engagement is a six-figure deck with no one to build it. We ship a measured workflow in 90 days — and the audit that starts it is free.
Team
An investor who installs the AI — with engineering behind him.
FO
Francisco Oteíza
Founder & Principal
Eight years across private equity, investment banking and a ~$4B family office; most recently Principal at Villarica Partners, where he built a co-investment book $0→$350M+ at 1.53× net MOIC and held LP-side relationships with Apollo, KKR and CD&R. Stanford GSB MBA '24. Now a founder who ships production AI. The wedge in one line: he has underwritten the deals — and he builds the systems.
Stanford GSB MBA '24Ex-Villarica Partners$350M+ co-invest bookShips production AI
Connect on LinkedInAI engineering partners
A vetted bench of senior ML and software engineers who do the production build — integration, governance, evals — alongside Francisco. Named on the SOW for your engagement.
Vendor-neutral by design
We're not reselling a platform. We pick Copilot, ChatGPT Enterprise, Claude or Gemini on the merits for your data and use case — so the recommendation is yours, not a quota's.
5 days
From kickoff to your AI Efficiency Map
Venture Core SLA
90 days
To one workflow live, with the delta measured
Venture Core SLA
95%
Of AI pilots stall before production — the gap we close
Industry benchmark
We work under NDA from first contact. Engagements run in your environment or an isolated tenant — data-room contents, portco financials and LP data never train third-party models, and we default to enterprise platforms with no-training, zero-retention configurations (ChatGPT Enterprise, Claude, Copilot, Gemini). Access is least-privilege and time-boxed, every action is logged for audit, and credentials are revoked at handoff. For diligence we can operate inside your VDR rather than exfiltrating documents.
That's exactly the wedge. ~95% of pilots stall before production — the problem is almost never the model, it's the process, integration and governance around it (~70% of AI value sits there). We take a stalled or siloed pilot and either get it to production with a measured before/after, or tell you honestly the workflow isn't where the value is and point you at the one that is.
A Big-4 engagement is a six-figure deck with no one to build it. We do the opposite: a free audit, a fixed-fee diagnostic, and then we build and deploy the workflow into production ourselves — integrated, governed, measured. We're led by a PE operator, so the recommendation is in your P&L language from the first meeting. And our fee is fixed-scope, not a meter.
The audit is free, so the first step costs nothing. On the build we carry a build-fee risk reversal: if we don't deliver the measured improvement we scoped against your day-one baseline, you don't pay the build fee. We define the success metric and baseline together, in writing, before we start.
Mid-market PE firms and their portfolio companies are the core. We also work with family offices (manual reporting and aggregation are a confirmed whitespace) and high-opex SMEs, which funds typically reach us through — buy the playbook once at the fund, deploy it across portcos.
The audit returns your AI Efficiency Map within 5 business days; slots are limited each month. To begin we need a short intake on your target workflow (the form doubles as that intake) and a 30-minute scoping call. No data-room access is required for the audit itself.
Free AI Efficiency Audit
Find the 3–5 places AI pays back first — at the fund and inside your portfolio.
Tell us about one portfolio company or the fund. In five business days you get a prioritized AI Efficiency Map — each opportunity sized in hours, dollars and cycle time. Yours to keep.
- A prioritized map of your 3–5 highest-P&L AI workflows
- Each one sized in hours / $ / cycle time
- No obligation, normally $2,500 — free, limited monthly slots
Prefer to talk first? Book a 15-minute call.
Get your free AI Efficiency Audit
The intake doubles as your audit brief. Takes about a minute.