Enterprise AI Platform
Outcomes
The second project costs a fraction of the first
Shared retrieval, authentication, evaluation and monitoring mean each new use case builds on infrastructure rather than recreating it.
Governance applied once, everywhere
Policy on data handling, model access, human oversight and vendor use enforced by the platform rather than by asking each team to remember.
Shadow AI reduced
When sanctioned tooling is easier to use than unsanctioned tooling, people use it. Most enterprise AI risk originates in employees using consumer tools with company data because nothing internal was available.
Cost visible per team and use case
Attribution and budgets, so AI spend is a managed line rather than a surprise.
What we build
Governed model gateway. A single access layer to approved models — commercial and self-hosted — with authentication, logging, rate limiting, cost attribution and policy enforcement. Teams get access without each of them holding provider credentials.
Shared retrieval layer. Common document ingestion, embedding and indexing with permission-aware retrieval, so every use case does not rebuild RAG from scratch.
Reusable component library. Agent scaffolding, evaluation harnesses, prompt management, guardrail modules.
Evaluation infrastructure available to every team, so quality is measured consistently across the organisation rather than defined per project.
Governance and approval workflow. How a new use case gets proposed, risk-assessed and approved, proportionate to its risk rather than uniformly heavy.
Self-service surfaces for non-technical teams — sanctioned assistants and templates over approved data — which is usually where adoption actually comes from.
How it works
Weeks 1–3 — Requirements and architecture. Existing and planned use cases, security and compliance requirements, current infrastructure. The platform is designed against real demand rather than a reference architecture.
Weeks 3–6 — Core platform. Model gateway, authentication, logging, cost attribution. Usable early, because a platform nobody can use yet generates no feedback.
Weeks 6–10 — Shared services. Retrieval layer, evaluation tooling, component library, monitoring.
Weeks 10–14 — Governance and onboarding. Approval workflow, documentation, and onboarding the first two or three teams — whose experience reshapes the platform more usefully than any design review.
Ongoing. Expansion driven by demand, with a product owner. Platforms without an owner become obstacles.
Technology
Gateway: LiteLLM or a custom layer, providing unified access with policy, logging and attribution.
Models: commercial providers plus self-hosted open-weight models where residency or cost requires, routed by policy and task.
Retrieval: shared vector infrastructure with permission-aware access integrated into your identity provider.
Identity and access: your existing SSO and role model, extended rather than duplicated.
Observability: unified tracing and cost reporting across every use case on the platform.
Deployment: your cloud, your VPC, your data residency constraints.
Where this applies
Justified when several teams are building AI, when governance is becoming a bottleneck, or when the same infrastructure is being rebuilt repeatedly.
Not justified for one or two use cases. Build those directly. A platform for a single consumer is overhead wearing a strategic label, and we will tell you if that is your situation.
How we scope and price
Fixed scope per phase, quoted after a requirements assessment. Cost is driven by organisational complexity, security and compliance requirements, and the number of teams onboarding initially. We build in phases with a usable gateway early rather than delivering a complete platform at the end, because the feedback from real teams changes the design.
Frequently asked questions
Two signals: teams rebuilding the same components, and governance reviews taking longer than builds. Absent both, you are early — and building early is a common and expensive mistake.
Sometimes, and we will tell you when a product fits. Vendor platforms handle common cases well and constrain unusual ones. The decision usually turns on how specific your governance, residency and integration requirements are.
It extends it. Identity, access, monitoring and deployment should reuse what you already run. A platform that introduces a parallel stack creates two problems.
An internal owner, ideally a small platform team. We build for operability, document thoroughly, and train your people. Ongoing support is available where an internal team is not yet in place.
The gateway is usually usable within six weeks. Full shared services take longer. We sequence so value arrives before completion.
The opposite — the gateway abstracts providers, so switching or routing across several becomes a configuration change rather than a rewrite. Reducing that lock-in is a common reason to build one.
More AI services
AI Strategy Consulting
Turn scattered AI ambition into a sequenced, costed plan. We decide what to build, what to buy, what to ignore, and in what order.
AI Readiness Audit
A 3–4 week assessment of your data, systems and processes that returns a ranked, costed list of AI use cases and an honest verdict on what you can deploy now.
Agentic AI Automation
We build AI agents that complete multi-step work inside your systems — with defined scope, human checkpoints, and evaluation. Deployed to production, not demos.