Product teams are integrating LLMs independently. The result is duplicated integration work, inconsistent quality and safety, no shared evaluation, and uncontrolled, unattributed cost. There is no governed path from prototype to production.
Provide a single internal LLM platform that lets teams build LLM features quickly while meeting shared standards for quality, safety, latency, and cost.
| Objective | Example target metric |
|---|---|
| Reduce time to ship an LLM feature | First feature live via platform within one quarter |
| Centralize and attribute spend | 100% of LLM spend routed through the gateway and attributed to a team |
| Raise quality and safety | All Tier 1 and Tier 2 features pass the eval rubric before GA |
| Control cost | Per-feature cost guardrails with alerts and caps |
| Reduce duplicate integration work | Shared SDK adopted by target teams |
Targets above are planning examples to be set with stakeholders, not reported results.
In scope: gateway and routing, provider abstraction, prompt and template management, evaluation harness, guardrails (safety, PII), cost attribution and caps, observability, a shared client SDK.
Out of scope (initial): model training, fine-tuning infrastructure, and a custom front-end. These are fast-follows.
Platform engineering, product teams, security, legal and compliance, FinOps, and the responsible-AI governance function (see Responsible-AI-Governance-Framework).
| Phase | Outcome |
|---|---|
| M1 | Brief approved, architecture agreed, providers selected |
| M2 | Gateway MVP: routing, auth, basic observability, cost attribution |
| M3 | Guardrails and evaluation harness integrated |
| M4 | Alpha with one design-partner team |
| M5 | Beta with three teams; SDK published |
| M6 | GA with documented standards and on-call |
Tracked in risk-register.csv. Key dependencies: provider contracts and quotas, security review of data flows, and the governance gates from the Responsible AI Governance Framework.