Frontier models, wired into systems
your business already runs.
The model is not the integration. The integration is auth, streaming, cost controls, eval gates, provider routing, fallbacks, regional residency, and a feedback loop into the next prompt change. We build the integration.
LLM integration is the work between “the API responds” and “the feature ships to a regulated production environment.” It includes auth (SSO, per-tenant rate limits), streaming (SSE/WebSocket plumbing through your stack), cost controls (caching, prompt size budgets, model fallback), eval gates (regression on prompt or model change), and provider routing (per-task model choice, regional pinning, failover).
An integration platform, not a one-off feature.
A production LLM integration
Auth, streaming, cost controls, retry logic, fallback routing — running in your environment, owned by your team after handover.
Eval gates in CI
Prompt and model changes can't deploy without passing your eval suite. Drift is caught before users feel it.
Cost engineering
Caching strategies (semantic + standard), prompt size budgets, model selection per task. Most engagements cut spend 30–50% versus the naive integration.
Provider portability
Switch models without rewriting application code. Per-task routing across OpenAI, Anthropic, Bedrock, Vertex, Azure, OpenRouter, vLLM.
Compliance posture
Region pinning (EU, US, regulated jurisdictions), audit logging, no-retention agreements. Designed around the audit, not retrofitted.
The plumbing, in detail.
- Provider clients
- Thin, typed wrappers around OpenAI, Anthropic, Bedrock, Vertex, Azure OpenAI, OpenRouter, and self-hosted (vLLM, TGI).
- Routing
- Per-task model selection, fallbacks, region pinning, retry-with-degrade. Configured in code, observable in traces.
- Caching
- Semantic cache (vector-based) for high-volume reads. Standard request cache where keys allow.
- Cost telemetry
- Per-tenant, per-feature, per-model. Surfaced in your existing observability stack (Datadog, Honeycomb, Grafana).
- Eval
- Prompt eval suite + LLM-as-judge for quality drift. CI-integrated. Blocks deploy on regression.
- Compliance plumbing
- Region pinning, request/response logging with PII redaction, no-train guarantees, model attestation tracking.
How it runs
A global asset manager wanted research-assistant features inside their existing portfolio platform. We integrated three model providers behind a routing layer, added a semantic cache that cut spend ~40%, and shipped eval gates that caught two prompt regressions before they reached production.
Global asset manager · Multi-provider integration platform
What buyers actually ask
Can you deploy on-premise or in our VPC?
What does an LLM feature actually cost?
SOC 2, HIPAA, ISO 27001 — can you work within these?
How do you handle prompt regressions when you swap models?
Talk to an engineer, not a salesperson.
30 minutes. No slides. Bring an architecture, a stalled roadmap, or a vendor proposal you want a second opinion on. We'll tell you what we'd do.