Quick Answer
Most private equity AI builds, on Claude, ChatGPT, Copilot or in-house, still read board packs and monthly submissions, so the answers are fluent but weeks out of date. The fix is not a different model. It is a live, structured portfolio data layer underneath the model a firm already uses, queried in place. Planr connects to portfolio company systems, normalises the data and serves it to that AI through MCP (Model Context Protocol).
"Like finance in the 1980s"
One operating executive at a buy-and-build group compared tracking his value creation plan to finance in the 1980s: four weeks to close month end, then start all over again.
His steering committee decks take weeks to build, so by the time the meeting happens, the team is debating a picture that is already out of date.
This is not a firm that is behind. It has a formal value creation plan, a large group of operating companies and a clear appetite for AI. It has the same problem almost every PE AI project is about to hit: the model is new, and the inputs are not.
The AI build that reads old numbers
A lower mid-market firm we spoke to did everything right. It built its own AI agent for portfolio monitoring, and the agent works well on board books.
But its portcos run on a patchwork of legacy ERPs. So before the agent sees anything, someone at each company exports the numbers, keys them into an Excel tracker and sends them up. What the firm wanted was an early warning system. What it built was a faster way to read old numbers.
Other firms are building on Claude directly, and we think that is the right instinct. One US mid-market portfolio team told us it now stands up Claude-built dashboards for its smaller portcos because they can be live within 30 to 60 days and cost almost nothing. Their own description: not perfect, not sustainable, but moving. The plan is to spend a year learning what the teams actually use, then put a proper data layer underneath.
The wider data says the same. Accordion's PE AI Adoption Benchmark, surveyed in Q2 2026, names data infrastructure "the clear frontrunner" among barriers to AI adoption, and finds only 6% of portcos arrive ready for AI deployment within 100 days.
Why we built Planr
Planr's founders built and sold an HR software company to a US growth equity firm, then stayed on its board as operating partners. Every quarter they sat in front of reporting that was two to three months old. Planr is the tool they wanted in that room.
What the data layer does
Planr connects directly to portco CRMs, ERPs, HRIS and finance systems through 80+ pre-built API integrations, including Salesforce, HubSpot, NetSuite, QuickBooks, Xero and Acumatica. Most connections are live the same day. Where a portco has no modern API, Planr ingests board decks, Excel files and emails instead.
Everything lands in one data model, so ARR at one company means the same thing as ARR at the next. Each metric carries a plain-English description, so your AI knows what it is looking at.
Then Planr serves that data to your AI through MCP, the open standard for connecting AI assistants to data sources. Your AI asks, Planr answers from the portfolio layer, and nothing is copied into another data lake to host and secure.
Two things matter most to the people who have to stand behind the numbers. As one portfolio executive put it to us, the worst thing you can do is send the wrong number. So portco teams review and approve their data before the GP sees it, and a deterministic maths layer means the same question returns the same figure every time, traced back to source.
What changes when the data is live
At one portfolio company, Planr flagged a miss on EBITDA and bookings four weeks before it appeared in the board pack. Because forecasts are built from live pipeline data, we have flagged shortfalls of 40% against target as early as the first quarter of a financial year, with eight to ten months left to change course.
The reporting burden drops too. A US roll-up with 30 entities used to spend three and a half weeks of FP&A time producing its board packs. With every ERP and CRM connected, it now takes a day.
The effect shows up in returns. One mid-market fund chose to test Planr's value creation tools before rolling them out: four of its 11 portcos went on, seven stayed off. Over more than a year, monthly MOIC at the four grew 23.7% faster. It is one fund and a small sample, but it was a side-by-side comparison, not a case study picked after the fact.
Live data also shows the waste that monthly packs hide. Teams we speak to regularly discover projects months after they started, with the same idea running in three places and paid for three times.
Firms using Planr today are already querying it from Claude through Planr's MCP server to run live, custom analysis. One firm's first request was simple: a guide so anyone using it knows what each dataset means. That is why every data model carries a plain-English context field your AI reads before it answers.
| Measure | With board packs | With live data |
|---|---|---|
| EBITDA and bookings miss | Seen in the board pack | Flagged 4 weeks earlier |
| Forecast shortfall against target | Seen at year end | 40% shortfalls flagged in Q1, with 8 to 10 months to act |
| Board packs across 30 entities | 3.5 weeks of FP&A time | 1 day |
| Monthly MOIC growth (4 portcos on, 7 off) | Baseline | 23.7% faster |
When you should build it yourself
Some firms should. The largest global sponsors run their own in-house portfolio platforms, and if you have a dedicated data engineering team and a small number of portcos on similar systems, building the pipes can make sense. Even those firms tell us the maintenance never stops.
For most mid-market firms, it does not. Ten to 30 portcos on different stacks means dozens of connectors to build, maintain and secure, and every acquisition adds more. The engineers who build them are the same people you wanted working on AI use cases.
Try One Question
Pick one portfolio company. Ask your AI what its pipeline coverage was last Friday.
If it cannot answer, or answers from the last board pack, that is the gap. Book a 30-minute call and we will show you the same question answered from live portco data, in a live demo.
Book a callRelated Reading
Analytics in Private Equity, Where Finance-Only Data Runs Out
Frequently Asked Questions
Why does AI in private equity need live portfolio data?
An AI tool can only answer from the data it can see. If that data comes from monthly board packs, every answer is weeks behind. Live portco data lets the same AI flag misses early, track value creation as it happens and support decisions while there is still time to act.
What is MCP and how does it connect portfolio data to AI?
MCP (Model Context Protocol) is an open standard that lets AI assistants query external data sources directly. Planr exposes normalised portfolio data through an MCP endpoint, so a firm's own AI tools can query live portco data without copying it into another system.
Does Planr replace our AI tool?
No. Planr is the data layer underneath it. Your firm keeps the AI assistant, workflows and agents it has built. Planr supplies the live, structured portfolio data those tools need to give current, auditable answers.
Which AI tools does Planr work with?
Planr's MCP endpoint is in use with Claude today. MCP is an open standard supported by a growing number of AI platforms, so the same data layer can serve other MCP-compatible tools your team adopts.
What about portfolio companies without modern APIs?
Planr ingests board decks, Excel files and emails for those companies, then normalises the data into the same model as API-connected portcos.
Is our portfolio data secure?
Planr is SOC 2 Type 1 certified. Portco teams approve data before the GP sees it, the data is queried in place rather than copied into a new data lake, and every number traces back to its source.
