Enterprise AI Platforms
We build governed AI platform layers with observability, evaluation, and cost controls for teams running multiple AI systems.
Where the operational bottleneck appears
- After multiple AI features reach production, one-off integrations create blind spots: no shared traces, no consistent quality checks, and no single view of what systems each agent can access.
- Without per-feature usage tracking, costs drift unpredictably and budgeting becomes reactive. Teams only see spend spikes after invoices arrive.
- Security and compliance reviews become slow because data access policies live in separate codebases with inconsistent enforcement and limited auditability.
We build a shared platform layer that every AI feature routes through, regardless of which team owns it. The gateway handles authentication, policy checks, usage accounting, and standardized request/response instrumentation before traffic reaches hosted or self-hosted model providers. On top of that, we implement centralized observability and evaluation pipelines: traces, latency, and cost are captured per feature, and quality checks run automatically when prompts or models change. Guardrails define allowed data scopes and actions for each feature, so governance is enforceable as configuration rather than tribal knowledge. Cost controls are applied as budgets and rate limits by team or feature, with clear reporting for engineering and finance stakeholders. This turns AI operations into a managed platform capability with the same operational discipline as other production systems, making it practical to scale from prototypes to multiple business-critical AI workloads.
graph TD
F["AI Features/Agents\nAgentic AI, RAG, LLM Apps, Voice AI"] --> G["Gateway/Middleware Layer\nauth, guardrails, cost tracking"]
G --> O["Observability & Evals\nlogging, tracing, automated quality checks"]
O --> M["Model Providers\nhosted + self-hosted"]
O --> A["Admin Dashboard\nusage, cost, quality metrics"]A common gateway gives every feature consistent auth, guardrails, and accounting before model calls execute. Because all traffic is centralized, teams can enforce per-team budgets and shared governance policies across independently built AI products.
Stack selected for this delivery pattern
Example: A client had three production AI features owned by different teams. Each feature called model providers directly, so cost reporting was fragmented, evaluation criteria were inconsistent, and guardrails varied by codebase. We introduced a shared gateway layer and routed all AI traffic through it. Every request now logs feature identity, latency, token usage, and estimated cost in a centralized trace pipeline. Data-access policies are enforced at the gateway before prompts are sent, and per-team budgets are applied through rate and spend controls. We also added an evaluation workflow that runs when prompts or models are changed, flagging quality regressions before release. The operational shift was immediate: teams could compare quality and cost across features using one dashboard, security reviewers had an auditable control point, and platform owners could tune provider selection without rewriting each product. The organization moved from isolated prototypes to governed production AI operations.
Service-specific answers
Not yet — this becomes valuable once you have two or more AI features or agents in production and need shared cost, quality, and security visibility across them.
No — the gateway pattern is provider-agnostic by design, so you can mix hosted and self-hosted models without rewriting every feature.
It gives your security team a single, auditable answer to what data and actions every AI system in your company can touch — rather than that answer living in each team’s code.