Services

What we do.

Three practice areas. Each grounded in the same foundation: technical depth in service of institutional decisions.

01

AI and Technology

Who this is for

PE sponsors and portfolio company leadership teams at services businesses with an exit horizon, and institutional clients seeking to improve operational efficiency across diligence, deal sourcing, and reporting.

Third Foresight is the AI and technology practice for the private markets industry. We build proprietary platforms for PE-backed operators where the platform is the argument for a higher exit multiple. We design and deploy AI-powered workflow systems that reduce manual process time across diligence, deal sourcing, and portfolio reporting for institutional clients. We assess AI claims at the architecture level in contexts where the answer moves a multiple. The team that builds these systems is the team that evaluates them.

Platform Builds for PE-Backed Operators

We design and build proprietary technology platforms for operators who need software to support a materially higher exit valuation. We manage the full lifecycle: discovery and stakeholder interviews, product requirements documentation, architecture design, and production deployment.

Every build is designed to pass Big 4 technical due diligence on exit. The commercial logic is direct: acquirers pay 8 to 12x EBITDA for technology-enabled businesses and 4 to 6x for pure-service operators. The platform is the difference.

Entry point

Phase 1 engagements, product requirements document and architecture design, are available as a scoped, fixed-fee commitment. No obligation to proceed to a full build.

AI Workflow Tools for Institutional Clients

We build tools, automations, and workflow systems tailored to how institutional clients actually operate. Engagements range from configuring and deploying best-in-class existing tools to building bespoke systems where the client's needs warrant proprietary development.

We also advise institutional teams on AI strategy: how to evaluate tools, how to build internal capability, and how to avoid adoption patterns that look good on paper and create operational risk in practice.

Every system we build has one goal: measurable reduction in manual process time and improvement in output quality.

02

Due Diligence

Who this is for

Institutional LPs evaluating a commitment to a fund manager, and PE or VC firms evaluating software or AI-driven acquisition targets and portfolio investments.

We conduct institutional-grade operational and technical diligence for both sides of the private markets equation. For allocators, we run full ODD on fund managers assessed against ILPA 3.0, IPEV, and IFC standards and deliver a clear, accountable verdict before capital is committed. For deal teams, we evaluate the AI and software claims inside acquisition targets and tell you precisely where the architecture holds and where it does not. We have been on the other side of this process. We know what your counterparts will find before they find it.

Operational Due Diligence

We conduct full institutional ODD on fund managers, assessing governance and organizational structure, operational controls and NAV procedures, valuation methodology, treasury and liquidity management, IT infrastructure, cybersecurity posture, and business continuity planning.

Our process is benchmarked against ILPA 3.0 principles, IPEV valuation guidelines, and IFC operational standards. Deliverables include an ODD summary report, a conditions precedent framework for investment committee review, and recommended side-letter protections.

The accountability difference

We take responsibility for our conclusions. Every engagement is backed by a $2M practice liability cap. You are not buying a software tool you have to operate and interpret. You are outsourcing the function, the judgment, and the accountability.

Technical Due Diligence

We assess whether a company's AI or software claims hold up under institutional scrutiny, covering architecture, data isolation, engineering team depth, AI governance, hallucination controls, customer AI readiness, and competitive moat.

We apply our Three-Category Framework, with direct valuation implications:

AI-native

Proprietary data flywheel, compounding moat, defensible IP. The architecture generates value that compounds and is difficult to replicate.

AI-integrated

Genuine third-party AI adoption with real operational uplift. Valuable, but the advantage does not compound and is easier to displace.

AI-washed

Legacy infrastructure with a large language model layered on top. The narrative is convincing. The architecture is not.

The difference between these categories regularly drives a 2 to 4x spread in justified revenue multiples. We provide a technical opinion on where a target sits, and we back that opinion with a $2M liability cap.

03

Manager Readiness

Who this is for

Emerging fund managers at Fund I or Fund II stage preparing to raise from institutional LPs.

Conviction gets a manager to Fund I. Institutional infrastructure gets them to sovereign LPs, pension funds, and fund-of-funds. We assess where a fund's operational readiness falls short of what the next class of LP will require, and we close those gaps before the roadshow opens. Data room, governance, LPAC structure, AI thesis validation. The diligence your LPs will run on you is a known process. We have seen it from their side. We run it on you first.

We assess and remediate a manager's institutional readiness before the fundraising roadshow. This covers data room quality and organization, governance documentation, LPAC structure and LP protections, and operational readiness benchmarked against what large allocators require.

The goal is straightforward: you should not learn what is missing from your data room when you are sitting across from a sovereign LP. You should learn it from us, with enough runway to fix it.

Pricing   Engagements start at $10,000 to $15,000.

Not sure which engagement fits?

Describe what you are working on and we will tell you honestly where we can help.

Get in Touch