Private equity and venture
You bought the company. You stillcannot see inside it.
The thesis was underwritten on numbers nobody can see today. The data sits in fifteen systems nobody ever connected, you ask a question and wait a week, and by the time the answer lands the opportunity has moved. Every operating partner has lived it. Nobody has priced it.
The visibility tax
Charged at every turn of the cycle.
Compression in internal rate of return is not only a market problem. A large part of it is a latency problem, and latency has a price.
What the thesis promised
What the quarter delivers
A rapid hundred-day value-creation plan.
Six months standing up reporting before the first real decision gets made.
Operating alpha from bolt-ons, integrated on day one.
Three enterprise resource planning systems, two customer relationship management systems, and no shared definition of a customer. Alpha arrives in year three.
Early warning the moment the thesis drifts.
Discovery at the quarterly review, six weeks late. By then the miss is structural.
A disciplined exit on clean, defensible data.
Six months of data-room reconstruction, and a discount the buyer keeps.
Blind spots become lag. Lag becomes discount. And the discount comes out of your multiple, not the buyer’s.
The cycle
Capital cannot exit, so it has to be improved in place.
That is the whole job now, and it is why operating improvement stopped being the nice half of the return and became the load-bearing half.
$3.8T
in unrealized value
Roughly 32,000 unsold buyout-backed companies sitting on general-partner books globally. The number has nearly doubled in five years.
Source: Bain Global Private Equity Report, 2026
6.6 years
median hold period
The highest on record. More than half of all buyout inventory has been held over four years, and almost forty percent over five.
Source: McKinsey Global Private Markets Report, 2026
~11%
distributions as a share of net asset value
Down from a 29 percent average across 2014 to 2017, and below 15 percent for four consecutive years. Money is not flowing back.
Source: Bain 2026; MSCI
53%
of limited partners cannot re-up
Existing commitments limit new allocations, up fifteen points year on year. Distributions is the most used term in fund pitches. Artificial-intelligence-driven value creation is second.
Source: Bain / PEI 2026; Coller Capital Winter 2025/26
The largest firms already run this play. Every one of them runs it by hand.
Real-time portfolio intelligence exists today at the top of this asset class, and it is built with full-time staff and embedded engineers, an army per company. That is precisely why only firms at that scale can afford one. Nobody had made it autonomous. That is the part SQOR already solved, which is what puts the same capability inside reach of a mid-market fund rather than only a flagship.
How it lands
From the data room to the day you sell it.
Before you buy
Diligence
- Read-only ingest inside the deal window, on the target’s own data.
- Thousands of metrics surfaced automatically, rather than the ones the seller chose to show you.
- Thesis assumptions tested against real transaction history before the investment committee meets.
The first hundred days
Day 1 to Day 100
- Day one, data connected. Week two, metrics live and goals set against thesis targets.
- Week three, the causal engine is running and courses of action are scored by impact, confidence and cost.
- Week four, operating partners receive nightly exceptions instead of monthly packs.
Steady state and exit
Hold through sale
- Every night, deviations ranked by thesis impact, so the monthly review becomes a decision meeting rather than a reading meeting.
- At exit every metric is already live and already auditable, so a buyer queries the company’s own data and gets the same answer you do.
- Shorter quality-of-earnings work, a smaller markdown, and the multiple holds.
Diligence normally runs on what the seller prepared. Own the instrument, and it runs on what is actually true.
The approval path
Start with one. The rest will ask to be next.
Pick the company where the help is needed most, or the one where it rolls out easiest, and use it as the proof. At the next quarterly review that company has daily visibility while the others are still pulling spreadsheets. Nobody has to be convinced by a deck after that.
Week 1
Diligence with the company’s chief financial officer and operators. Agree the first use case, capture the top twenty-five to fifty questions leadership actually asks, and confirm the initial sources.
Week 2
Data ingestion and schema generation. Agent and model refinement tuned to the use case. Testing begins wherever the data is provided.
Week 3
Delivery to end users, with structured feedback at scale from the people who own the numbers.
Week 4 and beyond
Incorporate the feedback, expand the scope, and begin the next company. The platform gets sharper with every company added.
If the first company does not deliver, stop there. No migration to reverse, no infrastructure left behind, no team to unwind. That is the whole risk you are taking.
Questions
What sponsors and operating partners ask.
What is the best way for a private-equity firm to get visibility across portfolio companies?
The obstacle is never the reporting template, it is that portfolio companies do not share a chart of accounts, a system landscape or a definition of a customer, so nothing rolls up without manual normalization. The approach that works is to conform each company’s data to one model automatically rather than asking every company to standardize first. SQOR.ai connects read-only to each company, generates that company’s measurement layer from its own data, reconciles every figure to its own ledger, and then reports each one up to a fund-level view on shared definitions. No portfolio company is asked to change its systems, and no fund team is asked to reconcile spreadsheets.
How does SQOR.ai help a value-creation plan?
By removing the months normally spent building the visibility the plan already assumed it had. A hold period is finite, and most of the early part of it goes into standing up reporting rather than pulling levers. SQOR compresses that to weeks, so pricing discipline, cost structure, working capital and commercial execution are all measured while there is still time on the clock to act on them, with the drivers behind each one computed rather than debated.
Does every portfolio company have to use the same systems?
No, and that is the point. Companies keep the enterprise resource planning system, the customer relationship management system and the warehouse they already run. SQOR reads them read-only, wherever they are, and conforms them to one model. Six acquisitions on five different systems across ten sites resolve into one portfolio view without a replatforming project at any of them.
Can a buyer rely on these numbers at exit?
SQOR sits beside the audited books and never writes to them, and every figure traces to its source record, its calculation and its logic with the date it was computed, reconciled to the ledger to the cent. That is what shortens a quality-of-earnings exercise: the diligence is run against live, traceable numbers rather than a reconstruction assembled six months before the sale. It does not replace an audit and it is not represented as one.
How do we try this without committing the whole portfolio?
Prove it on one company. Pick the one where the help is needed most, or the one where it rolls out easiest, and use it as the proof. At the next quarterly review that company has daily visibility while the others are still pulling spreadsheets, and the conversation changes on its own. If the first company does not deliver, you stop there: there is no migration to reverse, no infrastructure left behind and no team to unwind. That is the entire downside.
What does it cost per portfolio company?
Onboarding is a nominal set-up fee that covers the proof of concept, and the subscription starts at the twenty-five-user tier at $48,960 a year, priced on query volume rather than seats, with the data warehouse included and seats unlimited. It works out to roughly $163 per active user per month regardless of headcount. Fund-level agreements and portfolio volume economics are available.
The black box is a choice. So is opening it.
Sixty minutes with your operating team, and one portfolio company to point it at. We come back with the business case on that company’s own numbers.