Service
LLM middleware built into your backend
A controlled server-side layer every AI request passes through — identity, policy, provider routing, logging, and cost controls before any model call.
Who this is for
Engineering teams that have shipped an AI demo or POC and need a production boundary before expanding features — especially multi-tenant SaaS with compliance, cost, or provider failover requirements.
Problems we solve
Common failure modes when copilot, retrieval, or middleware features are bolted on without an integration plan.
- API keys and model calls exposed from the frontend — no central place to enforce policy or cut off abuse
- Each feature team reinvents auth checks, rate limits, and logging in slightly incompatible ways
- No visibility into token spend per tenant, per feature, or per workflow when finance or product asks what AI costs
Typical deliverables
- Middleware service or module in your repo — API routes, microservice, or shared library behind your existing session or JWT auth
- Provider abstraction with routing, streaming, caching, and failover across OpenAI, Anthropic, Gemini, or self-hosted endpoints
- Structured logging and seed eval hooks, and dashboards for latency, error rate, and tokens per successful user action
- Runbooks for on-call — kill switches, provider outage fallbacks, and prompt rollback without redeploying the whole app
How we deliver
Your eng team stays on the roadmap. We handle the AI integration layer — scoped sprints, PRs to your repo, and handoff docs so your team can operate what we ship.
Middleware is usually the first integration boundary we recommend: every later feature — RAG, copilots, agents — shares the same auth, logging, and cost envelope. We map your current architecture in the audit phase, ship a working proxy against your real stack, then expand to the first workflow-bound feature behind feature flags.
Step 1
Technical audit
Map your architecture, API boundaries, data flows, and auth model. Identify the lowest-risk, highest-value integration point.
Step 2
Architecture & prototype
API contracts, middleware design, and a working proof against your real stack — validated before full build commitment.
Step 3
Build & deploy
Production code in your repo. Staging, load testing, and canary rollout behind feature flags — with runbooks for your team.
Step 4
Operate & expand
Monitor latency, cost, and output quality. Iterate on evals and prompts, then extend to the next workflow boundary.
Related guides
Deeper technical notes from our resources library.
LLM middleware: what it is, why you need it, and how to implement it
A practical guide to the server-side layer between your app and the model — auth, rate limits, routing, logging, and the patterns that keep AI features production-ready.
June 7, 2026
Langfuse for LLM observability — where it fits in your middleware stack
How to trace model calls, debug prompts, and run evals with Langfuse — integrated into server-side LLM middleware, not bolted onto a frontend demo.
June 8, 2026
What production-ready LLM integration actually means
A practical checklist for engineering leaders — beyond the demo and before you call an AI feature shipped.
May 15, 2026
Common questions
- Is this the same as Next.js middleware?
- No. We mean an LLM middleware service in your backend — an API route or module that owns model calls. It is unrelated to edge routing middleware, though many Next.js apps implement it as an API route under app/api/.
- Can we keep our current model provider?
- Yes. We design a provider abstraction in your codebase so you can route to OpenAI, Anthropic, Google Gemini, or self-hosted models — and swap or split traffic later for cost, compliance, or failover without rewriting product features.
- What does production-ready middleware include beyond the proxy?
- Auth enforcement before any model call, per-tenant rate limits and budgets, structured tracing tied to your existing observability stack, eval baselines for prompt changes, and defined failure behavior when a provider is slow or unavailable.
Scope an integration for your stack
Describe the feature you are planning — we will map architecture, effort, rollout strategy, and what production-ready means for your system.
Get an integration plan