Enterprise R&DFortune 500 R&D division

Private LLM platform for an enterprise research team

Anonymized engagement building a private, governed LLM platform for an enterprise research team that could not use public AI tools.

Tools / product used · Custom enterprise AI on Vestval architecture

Challenge

Researchers needed serious LLM-assisted workflows but could not put proprietary data into public models. Their internal IT had no LLM…

Solution

A private LLM platform supporting multiple model providers, internal data via governed retrieval, audit logs, role-based access, and…

Architecture

We designed a private platform with model routing, evaluation, observability and access control — and embedded engineers alongside the…

Timeline

4-phase implementation · Custom enterprise AI on Vestval architecture

Impact

Research team gained serious AI leverage without compromising data posture

Challenge

Researchers needed serious LLM-assisted workflows but could not put proprietary data into public models. Their internal IT had no LLM platform of record.

Objectives

Provide research-grade LLM workflows without proprietary data leaving the perimeter
Establish an LLM platform of record for the organization
Embed evaluation and audit at the platform layer, not per-team
Hand off a maintainable system to internal IT

Approach

We designed a private platform with model routing, evaluation, observability and access control — and embedded engineers alongside the research team for six months.

Solution

A private LLM platform supporting multiple model providers, internal data via governed retrieval, audit logs, role-based access, and evaluation harnesses for research-grade workloads.

Implementation approach

1
Platform architecture first
Model routing, retrieval, evaluation, observability and access control designed as one architecture — not a stack of point tools.
2
Governed retrieval layer
Internal data exposed via a governed retrieval layer with per-document access control, not raw vector dumps.
3
Evaluation as platform feature
Eval harnesses built into the platform so research workflows could be measured, not vibes-checked.
4
Six-month embedded engagement
Vestval engineers embedded with the research team for six months, with explicit handover-to-IT milestones.

Technologies used

Private LLM deployment
Model router
Governed RAG
Eval harness
Audit log
Role-based access

Outcomes

Research team gained serious AI leverage without compromising data posture
Platform became the organization's LLM system of record
Internal IT inherited a maintainable, well-instrumented system
Clear basis for compliance and audit conversations

Lessons learned

Build the platform, not the demo. Demos die; platforms compound.
Governance at the retrieval layer is the unsexy lever that makes enterprise AI defensible.
Embed for handover from day one. Without it, internal IT inherits a black box.