AI value creation in private equity
We identify and build AI-driven operational capability across PE-backed portfolios. Real problems, not AI strategy. Working solutions in weeks. A model that repeats across the portfolio.
Focus on real problems
Working solutions in weeks
Repeatable model
AI is active across portfolios, but it’s not moving EBITDA.
Most PE portfolios have plenty of AI activity. Very few can point to where it’s actually driving EBITDA.
Most AI stops at the first step. We build the full journey.
Automation is where almost every AI project starts, and where most of them stop. The value that actually moves a multiple sits in the stages after: the new ways of working that structured data unlocks, and the new revenue that owned capability creates. We build for the whole journey, not just the first step.
TFS Global didn’t stop at automation.
More than 70% of 14,000+ vendor invoices a month, processed with no human touch and reduced from weeks to near real-time.
The clean, structured data surfaced billing anomalies and cost savings the team could finally act on, work that simply was not possible before.
Invoice operations moved from a cost center to a value-added capability the business now runs on and keeps extending.
Patterns repeat. Deliverables accelerate.
Most vendors keep this part vague. We’d rather you know exactly where the lines sit before you sign anything.
Full discovery and build. We map the problem from scratch and ship the first working solution against a real success metric.
The same pattern, recognised. The model is already defined, so discovery shrinks and we move straight to fitting it to the company.
Pattern applied directly. By the third company, deployment is the main event and discovery has all but disappeared.
The asset survives the hold.
Most AI a portfolio company buys is rented: it works while you pay, and leaves nothing behind when you stop. What we build is owned, and that changes what it is worth at exit.
What the AI learned, the workflows, the trained models, the structured data: it is portco IP. It goes in the data room as an asset, not a license that expires when the vendor relationship ends, so a buyer is acquiring capability, not renting it.
The institutional knowledge that usually lives in a few people’s heads gets captured in the system. When a key operator leaves, the capability stays, which is exactly the kind of key-person risk a diligence team prices.
Generic software is a cost line. Custom, AI-driven process that compounds is an asset, and assets are what expand a multiple. It is the whole difference between AI activity and AI value.
You already know where the drag is.
The bottlenecks aren’t a mystery. Across portfolio companies they cluster into the same three shapes, and each one is a place where AI removes a constraint that labor or off-the-shelf software can’t. Here’s what they look like, and where we’d start.
High-volume work that needs judgment, not just hands.
Where we'd start: high-volume work that needs judgment, not just hands.
Data trapped across legacy systems, with no single view.
- Invoice processing
- Service scheduling
- Expectation handling
One view across the systems you’ve acquired: proposal consolidation and the knowledge capture that turns disconnected ERPs into a margin picture.
Margin lost between pricing, proposals, and follow-through.
Where we'd start: margin lost between pricing, proposals, and follow-through.
Start with a no-cost proof of value.
One win leads to another because you keep what each win learns. Every solution leaves behind clean data and context the next one builds on. Rented AI can’t do that; the learning walks away with the vendor.
An intro call with the operator. We walk their business, map the real operational drag, and pick the highest-ROI entry point together, agreeing on what a win looks like.
A mutual NDA and a scoped, read-only data share, plus a short technical session with their team. No ERP migration, and no rip-and-replace.
A functioning AI solution on their actual data, measured against the success criteria we set in week one. A clear go/no-go at the end, and a production scope only if it lands.
Built to be diligenced.
Every solution we build runs on our managed AI platform. That means the infrastructure, security, orchestration, and operations are solved before your first sprint begins. We spend your investment on solving your actual business problem from day one, not on assembling the plumbing.
SOC 2 aligned and maturing toward certification, tenant isolation by architecture, full audit trails, and background-verified people. The data-handling answers a questionnaire asks for, in one place.
Azure-native and fully managed: the compute, the model operations, and the security patching your portfolio company does not inherit. Architecture, isolation, and the managed-burden story.
Bring us one portfolio company.
