Private Capital Team
Your investment thesis. A stronger operating engine. We help private-equity firms turn AI opportunities into practical value-creation plans for their portfolio companies.
For operating partners, investment teams, and portfolio-company leaders.
AI for private-equity operations
Focus on the work that drives the business. We connect portfolio diagnostics, workflow redesign, and implementation to the priorities in your value-creation plan.
Find where AI fits the investment thesis. Assess workflow intensity, data readiness, systems, and management priorities across portfolio companies. Prioritise opportunities against implementation effort, operating risk, and potential business value.
A prioritised opportunity map with assumptions and accountable owners.
Connect AI agents to the systems and people who do the work. Redesign handoffs across finance, sales, service, and operations, with clear permissions, exception handling, and human approval where it matters.
An operating plan that connects each agent to a business workflow.
Target repetitive work in reporting, reconciliation, document handling, support, and shared services. Evaluate total delivery cost, including integration, model usage, quality review, and ongoing maintenance, before expanding a deployment.
A measured pilot with an agreed baseline for cost, speed, and quality.
Explore account research, sales preparation, customer onboarding, retention workflows, and pricing analysis. Keep commercial decisions accountable to management and measure results against the existing process.
A commercial workflow backlog tied to the portfolio company's growth priorities.
Across the investment lifecycle
Each stage asks a different question. The work should change with it.
Before the deal
Assess the target's technology, data, and operating model. Separate feasible workflow improvements from speculative AI upside, and identify integration or governance constraints before they enter the value-creation plan.
First 100 days
Agree the baseline, choose an initial workflow, assign a business owner, and define success criteria. Sequence a pilot around data access, security review, and the management team's capacity to adopt change.
During the hold
Extend workflows that demonstrate useful results. Reuse architecture across portfolio companies where systems and operating needs align, while keeping data access and company-specific responsibilities separate.
At exit
Document implemented workflows, measured outcomes, operating costs, controls, and ownership. Give the management team a clear account of what is running and what remains on the roadmap.
Built for operating teams
Bring us a portfolio priority: slow financial reporting, a fragmented sales process, service backlogs, or an integration challenge. Together, we define what to investigate, what to build, and what evidence would justify scaling.
Agree the scope, responsible owners, dependencies, and acceptance criteria before delivery. Assess the full cost of the solution alongside its expected value.
Design access controls, human approvals, audit trails, and escalation paths into the workflow. Production deployment is a separate decision from a promising prototype.
Questions from private-equity teams
AI can support specific workflows across diligence, portfolio operations, and commercial execution. The starting point is a measurable business process: establish the baseline, test a bounded change, and compare the result with its full operating cost. The right opportunities depend on each company's data, systems, and management priorities.
Start with one portfolio company and one workflow that has a clear owner, accessible data, and a measurable bottleneck. A portfolio diagnostic can help rank candidates before committing to a wider rollout.
The approach is to work alongside the sponsor's value-creation team and portfolio-company management. Integration requirements, access controls, and responsibilities are assessed before choosing tools or redesigning a workflow.
Scope is agreed with your team. An initial diagnostic can produce a workflow and data-readiness assessment, a prioritised opportunity backlog, a proposed pilot, and a measurement plan. Implementation follows the agreed scope and decision gates.
Agree the baseline and acceptance criteria before implementation. Track cycle time, quality, human review, exceptions, adoption, and total operating cost. Any financial impact should be validated with the company's finance team; savings, EBITDA uplift, and investment returns are not guaranteed.
Define data boundaries, permissions, retention, logging, and approval requirements during scoping. Keep sensitive actions subject to agreed human oversight and review security and legal requirements before a production rollout.
Private Capital Team
Tell us the companies, workflows, and priorities you have in mind. Start with a focused conversation about a portfolio diagnostic or a specific implementation opportunity.