Private Equity
AI an operating partner can actually deploy
Due diligence that takes days, not weeks. Portfolio companies that hit their 100-day AI plan instead of writing another one. AiRE rolled out across the portfolio you already own, and Labs built for the gaps nothing off the shelf fills.
Free diagnostic, three minutes. No follow-up unless you ask.
What AI for private equity means
AI for private equity means using machine learning, generative AI and data engineering to make better decisions, sooner, at each stage of the investment cycle: reading a data room in diligence, running a value creation plan across the portfolio, and preparing a company for exit. For an operating partner the point is not a model. It is a playbook that runs the same way in every portfolio company, so the value creation plan means the same thing in every board pack.
QuantSpark has done this work for funds and for the companies they own. It includes a modular AI vulnerability and opportunity assessment across a portfolio, a repeatable data-science playbook for a European software-focused investor, forensic revenue and churn analytics that supported a SaaS business through vendor due diligence, and a value-creation data stack built with Chronograph that compresses portfolio monitoring from days to minutes.
We are an AI consultancy of around 70 strategists, data scientists and engineers, building AI and analytics systems since 2016. The people who scope the work are the people who build it, and the fund or the portfolio company owns the code and the IP at the end.
Built around the operating partner's problem
Not a generic AI pitch. The four places funds and portfolio companies actually ask us to start.
Due-diligence automation
AI-assisted reads of data rooms, PPMs and management packs. Extract the numbers that matter and flag the risks worth asking about, before you sign rather than after.
Portfolio value creation
One playbook run consistently across every portfolio company, rather than reinvented deal by deal. AiRE deploys onto whatever stack the company already has, not a stack you have to buy first.
100-day plans that ship
A working AI rollout inside the first 100 days, not a slide deck promising one. Labs builds what is missing; AiRE deploys what already exists across the operating model.
AiRE for portfolio companies
The same diagnostic and rollout engine applied across the portfolio, so the value creation plan means the same thing in every board pack, whichever company is presenting it.
What we have built for private equity
Specific engagements, specific results. Anonymised by agreement where the client asked for it.
DataControl Platform: intelligent private equity data management
A mid-market private equity firm
DataControl Platform is QuantSpark's web application for PE firms: one place to collect, validate, visualise and sign-off portfolio data. One platform, one source of truth, fewer spreadsheets.
Building a private equity value-creation data stack with Chronograph
Chronograph
Returns now come from inside the portfolio company, not from cheap leverage. QuantSpark and Chronograph built a three-stage data stack that compresses portfolio monitoring from days to minutes and gives deal teams one live source of truth for every KPI.
Accelerating AI Adoption and Mitigating Risk Across Private Equity Portfolios
A leading private equity firm
QuantSpark delivered a modular AI Vulnerability & Opportunity Assessment for the firm's portfolio companies, providing a comprehensive view of AI risks, opportunities, and capability gaps to inform…
A rapid AI prototype to auto-file email attachments at a private equity firm
A private equity firm
A private equity firm built internal AI momentum with a lightweight prototype that automatically files inbound email attachments into its document management system.
Predicting churn to protect a compliance SaaS business
A health and safety compliance SaaS and accreditation business, owned by a UK private equity house
A health and safety compliance SaaS and accreditation business wanted to move from BI reporting to predictive retention. QuantSpark built a proof-of-concept churn model that identified the customers most likely to leave at renewal, enabling prioritised outreach.
Validating a private equity buy-and-build strategy with recurring-revenue analytics
A private equity owned B2B SaaS group
A private equity owned B2B SaaS group had grown by acquisition, but its business units tracked customers on different systems. Consolidated recurring-revenue analytics gave investors a like-for-like view that validated the buy-and-build thesis.
An interactive data cube supporting the sale of a SaaS business
A private-equity-backed SaaS business
Forensic revenue and churn analytics, delivered as interactive dashboards, withstood investor scrutiny and reinforced the valuation of a SaaS business during its sale.
A repeatable data-science playbook across a private equity portfolio
A leading European software-focused private equity investor
QuantSpark built a repeatable data-science playbook for a leading European software-focused private equity investor, deploying cloud data platforms and machine learning across its portfolio to reduce churn, sharpen renewals and evidence growth.
Standardising revenue policies for a buy-and-build exit
A private-equity-backed professional services group
Standardised revenue-recognition, work-in-progress and fee policies unified a multi-entity professional services group's reporting, simplifying integration and preparing it for exit.
How we work across the deal cycle
Funds usually ask us to start in one of three places. Each maps to a fixed step on our offer ladder, so the first commitment is small and the next one is earned.
Before you sign
Diligence questions that can be answered from data get answered from data. Consolidated ARR analytics gave a private equity owned SaaS group a like-for-like view of recurring revenue that validated its buy-and-build strategy, and a churn model for a compliance software business predicted 76 per cent of churn instances in test data. The deal team keeps the judgement and gets the evidence in days.
In the first 100 days
An AiRE Diagnostic gives a portfolio company a short list of AI bets ranked by value, feasibility and risk, with an honest read: roll out, build, buy or wait. First Agent then puts a production-candidate agent on the company's own stack in five weeks, built with its team so the capability stays when we leave.
Across the portfolio and at exit
The same diagnostic and rollout engine, applied company by company, turns one success into a portfolio programme. At exit, clean data carries value: we standardised revenue and fee policies for a PE-backed professional services group ahead of a clean exit, and the DataControl Platform gives firms one place to collect, validate, visualise and sign off portfolio data.
Proof, not promises
EBITDA improvement for a PE-backed retailer, through AI-driven pricing
Figures are drawn from completed QuantSpark engagements. Clients are anonymised by agreement; on a call we will walk you through how each number was measured and, where the client has agreed, put you in touch with a reference.
One ladder, each step earned
Every engagement starts small and earns the next step. Book a discovery call and we will tell you where to start, honestly.
Readiness Diagnostic
Free. Ten questions, a scored readiness band and an honest diagnosis. No follow-up unless you ask.
- Scored readiness band
- Plain-English diagnosis
- Recommended next step
AiRE Diagnostic
A short list of AI bets ranked by value, feasibility and risk, a recommended roadmap, and an honest read: roll out, build, buy, or wait.
- Ranked AI bets
- Value and feasibility scoring
- Recommended roadmap
- Honest build / buy / wait call
First Agent
A production-candidate agent, live on your own stack in five weeks, built with your team so you leave owning both the agent and the ability to build the next one.
- One core workflow mapped (week 1)
- One production-candidate agent on your stack
- Your people building alongside us
- Runbook, eval harness, code and IP, all yours
AiRE Rollout
Fractional AI implementation across your existing stack. From AI experiments to AI operations.
- Tool deployment on your stack
- Team enablement
- Governance and guardrails
- Monthly reviews and Slack support
Labs Discovery
A working prototype on your data, in your workflow. Not a slide deck. Validated value and a scale roadmap.
- Process and data mapping
- Working prototype
- Validated value case
- Scale roadmap
Labs Build
An enterprise-grade system embedded in your operating model. You own the code and the IP.
- Production build
- Integration and embedding
- Team handover
- 12 months support
Every project is scoped individually. Book a discovery call and we will provide a detailed proposal within 48 hours.
Private equity AI questions, answered
How is AI used in private equity?
Can AI speed up due diligence?
How do you create value with AI across a portfolio?
What results should an operating partner expect?
Do portfolio companies need new systems before they can use AI?
What does it cost to start?
Talk to an AI consultancy for private equity that has done this before.
Start with the honest read
Three minutes, ten questions, no follow-up unless you ask. Or talk to a human first.