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Private Equity & Portfolio Value Creation
AI across the investment cycle
Multiple expansion carried a decade of returns. The next fund will be judged on operating alpha, and on how fast you can prove it to LPs. Since 2016, Sigmoidal has built production AI for private equity deal teams and operating groups: diligence systems, consolidation engines, and research platforms, delivered by embedded senior teams that own the outcome.
Capabilities
Where AI earns its keep across the deal lifecycle
Most AI spend in private equity dies in pilots because it was aimed at a demo instead of a fund metric. We build against the numbers a fund actually manages: EBITDA growth over the hold, diligence cycle time, days from portco close to consolidated numbers, and the cost of LP reporting.
Deal sourcing and pipeline intelligence
Deal flow is a relationship business; ranking it is a data problem. We build scoring models trained on your fund's own record, deals won, deals passed, how both calls aged, so the pipeline ranks targets against your thesis rather than a banker's teaser. Partners spend Monday morning on the eight targets that matter.
AI-assisted due diligence
A diligence sprint is three weeks of associates reading a data room at midnight. We train models on your deal history to run compliance assessment, risk analysis, and deal scoring before the first folder opens, every flag linked to its source documents. For one global PE firm, the system reached 97% agreement with blind partner review on live deal flow, trained on 32,000 historical deals. Screening that once consumed an analyst's first week now takes an afternoon of verification.
Portfolio company value creation
The 100-day plan names the same levers every time: pricing, procurement, working capital, retention. We embed engineers inside portfolio companies to build the systems behind those levers: demand forecasting for the distributor, predictive maintenance for the manufacturer, a pricing engine for the software asset. That maintenance practice produced 72% less unplanned analyzer downtime across the monitored fleet for a global pharmaceutical leader. Scoped to the hold, built to show its value where a buyer will look at exit.
Financial consolidation and LP reporting automation
Twenty portcos, twenty charts of accounts, one LP deadline. We automate consolidation across entities and ERPs: for one PE client, 89% of portfolio financial consolidation now runs automated end to end, every figure traceable to the source ledger line. Quarter end stops being archaeology.
Liquidity forecasting and exit readiness
DPI pressure is not abstract. LPs count distributions, not marks. We build liquidity forecasting on the same consolidated, lineage-tracked data that runs your reporting, and exit-readiness systems that give a sell-side process clean, defensible numbers before the buyer's diligence team starts pulling threads. The expensive surprises are the ones found by the other side.
Research and investment intelligence
A chatbot that cannot show its sources will not survive compliance review. We build retrieval and synthesis systems over a firm's own filings, transcripts, and expert-call notes, every output traceable to a source document. At a global investment manager with more than $80B under management, research productivity rose 40% in year one, with 24% more names under coverage and no added headcount.
Proof
Numbers we will defend in the first meeting
Every figure below comes from a production system, measured by the client, and links to the case study that documents it. We will walk your team through the methodology, the baseline, and what broke along the way.
- 97%
- agreement with blind partner review on live deal flow, trained on 32,000 historical deals, for a global PE firm Case study
Client story
One portfolio, many ERPs, a close that stopped scaling
A private equity firm was consolidating monthly numbers from portfolio companies running different ERPs, different charts of accounts, and different ideas of what a closed month means. Every acquisition added another spreadsheet to the chain, and the finance team spent the close re-mapping trial balances by hand while LPs asked for more granularity, faster.
We built a consolidation pipeline that treats each portco ledger as a data source with a contract: entity and account mapping that is stored and reviewable, anomaly and completeness checks that catch broken balances before consolidation runs, and audit-ready lineage from every consolidated figure back to the source ledger line.
Today 89% of the consolidation workload runs automated end to end, and the close is measured in days rather than weeks. The judgment calls stay with the finance team, clearly flagged: adjustments, eliminations that need context, and the final sign-off. The pattern repeats: reporting automation pays for itself, then funds the value-creation work.
Engagement model
How an embedded team plugs into a fund
Every engagement starts with a two-week diagnostic: we map where hours and basis points are actually going, and come back with a build plan priced against outcomes. Then we embed: an engagement lead, three to five senior engineers, and a domain specialist who has worked inside a fund, operating in your stack (CRM, data rooms, portco ERPs) and on your calendar of IC cycles, quarter close, and exit timelines. Our teams work under information barriers, MNPI restrictions, and the confidentiality a fund owes its LPs. At the global investment manager where our research platform runs, the first production release shipped in week seven and the first year closed with zero material compliance findings.
Weeks 1-2
Diagnostic with the deal team or ops group, ending in a priced build plan tied to fund metrics.
Weeks 3-12
Embedded build inside your stack, with a working system in front of users by week six.
Ongoing
Production ownership, model monitoring, and quarterly value reviews against the plan.
Bring us one live problem
The first conversation is a working session with a partner who has built these systems. Bring a diligence bottleneck, a portco reporting mess, or an exit eighteen months out that needs a cleaner data story. We will tell you what a system would look like, what it costs, and whether it is worth building. If it is not, that is a short meeting instead of a long engagement.