Solutions / 03 — Backend Systems

The quiet layer
your AI runs on.

APIs, data models, async jobs, integrations, and the observability that makes production survivable. The unglamorous half of every AI product, done well so you don't notice it.

Discuss your backend See agents

What's included

Prototype to production, end to end.

The pieces that turn a clever notebook into a service your customers can rely on.

01 / 03

API development

Clean, versioned FastAPI services with validation, auto-generated docs, and service boundaries that evolve safely. Built to be read and changed by other engineers — including future you.

02 / 03

Auth & security

RBAC, multi-tenancy, secret handling, rate limiting, audit logging. The boring controls that turn out to matter the moment you sign your first enterprise customer.

03 / 03

Data & observability

Postgres data modeling, async jobs, Redis caching, structured logging, and metrics/tracing for both classic requests and LLM/tool flows. So when something breaks, you find it.

The stack

Boring tech, on purpose.

We pick proven primitives and let the AI be the new thing in the architecture.

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Python · FastAPI

Async services with strong typing and testable boundaries. Plays nicely with the rest of the AI ecosystem.

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Postgres · Redis

Postgres for the truth. Redis for the speed. Vector search where it earns its place — not by default.

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Observability built-in

Tracing for LLM flows, structured logs, usage telemetry, and the metrics on-call actually looks at.

Common questions

Asked before every backend build.

Why FastAPI and Postgres for AI products?
Async Python fits streaming LLM responses and tool calls, FastAPI gives typed and documented APIs, and Postgres covers relational data plus vector search in one place. Proven primitives, so the AI is the only new thing in the architecture.
Can you take over or harden an existing backend?
Yes. Auditing an existing FastAPI or Python codebase and adding multi-tenancy, auth, async jobs, or observability is a common engagement.
Do you build backends for non-AI products?
Yes. GymHive, our in-house membership SaaS, runs on the same FastAPI, Postgres, and Redis stack with no LLM in sight. The production concerns are identical.
What does observability include?
Structured logs, tracing across requests, LLM calls, and tool calls, usage telemetry, and the metrics on-call actually looks at — so you can always answer what the system did and why.

Next step

Working notebook, no backend yet?

We'll wrap it in the production layer it needs — and only the production layer it needs.

Start a project