AI Technology Advisory

AI leverage, inside your perimeter.

Your team already knows what they'd do with a capable model: read a data room overnight, draft the first pass of an IC memo, answer LP questions from ten years of fund documents. What stops them isn't capability. It's that CIMs, LP data, and portfolio financials cannot be pasted into a public endpoint, and your compliance officer is right about that.

Verified Metrics designs, deploys, and manages enterprise-grade AI infrastructure that runs entirely inside your firm: on-premise servers or your private cloud, open-weight models selected and tested for your workloads, retrieval over your own document archive, with the access controls and audit logs your auditors and LPs will eventually ask about.

We're not integrators discovering finance. We run this stack on our own diligence and valuation work daily. We're deploying for you what we already trust with our own client data.

Why Verified Metrics

• Nothing leaves the building. Air-gapped or VPC deployment within your security perimeter. No training on your data, no third-party endpoints, full data residency.
• Practitioners, not resellers. The people specifying your hardware underwrite deals and defend marks with the same systems.
• Scoped to payback. We start from your workflows (screening, diligence review, reporting) and deploy against the use cases with measurable return, not a platform looking for a purpose.
• Governed from day one. Access controls, audit trails, model evaluation, and acceptable-use policy ship with the system, not as a retrofit.

What this covers

• Use-case assessment: where AI genuinely pays inside your investment process
• Hardware specification and procurement: on-premise servers sized for fund workloads
• Model selection and benchmarking: open-weight models evaluated on your actual documents
• Secure retrieval over your deal archive, data rooms, and fund documents
• Governance build-out: permissions, logging, evaluation, and policy
• Team training, and managed support with model updates as the field moves

How an engagement runs

• Assessment: 2–3 weeks mapping workflows to use cases, with a build/buy/wait recommendation per use case
• Deployment: 6–10 weeks from hardware order to production
• Handover: your team trained, governance documented, support agreement in place
• Ongoing: managed updates as models improve, so your stack doesn't freeze in 2026

What you receive

• A production AI system inside your security perimeter
• A use-case roadmap with expected payback per workflow
• A governance pack: policies, access model, audit logging
• A trained team and a named support contact