Backend and platform engineering for AI

AI features are only as good as the platform under them. We build the APIs, pipelines, and multi-tenant infrastructure that let AI products run reliably and scale without surprises.

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Users, client apps, and third-party systems connect through APIs, queues, data pipelines, and tenant isolation, producing reliable AI features that are observable.

What backend and platform engineering for AI involves

The model is the smallest part of most AI products. Around it sit the APIs that serve users, the pipelines that prepare data, the queues that run slow work in the background, the integrations with third-party systems, and the controls that keep each customer's data separate. When AI products fail in production, it is usually here: a timeout nobody handled, an integration that silently stopped syncing, a tenant who could see data they should not.

We build that platform with the same care as the product. APIs are designed for the slow, variable responses that model calls produce, with streaming, timeouts, and idempotent retries. Long-running work moves to queues with visible state. Integrations track what they synced and recover from failures. Multi-tenant boundaries are enforced in the data layer, not left to application code to remember.

Everything is instrumented. Latency, errors, queue depth, and model cost per tenant are visible from the first deployment, so problems show up on a dashboard before they show up in a support ticket. For AI systems that need the next layer of quality and cost control, see production AI.

Problems we solve

An AI feature works in a demo but has no reliable backend to run on.
The system needs to serve multiple customers securely from one platform.
Background work, integrations, and data pipelines are brittle and hard to observe.

What's included

APIs
Data pipelines
Authentication
Queues and background processing
Third-party integrations
Multi-tenant systems
Cloud deployment
Observability

How we approach it

01
Architect
Design APIs, data model, and multi-tenant boundaries.
02
Build
Implement services, queues, and integrations.
03
Secure
Add authentication, access control, and environment separation.
04
Observe
Instrument the platform for latency, errors, and cost.

Decisions that matter

Design for slow, variable dependencies

Model APIs can take seconds to respond and occasionally fail. We stream where users are waiting, push batch work to queues, and set timeouts and fallbacks on every external call.

Isolate tenants in the data layer

In multi-tenant systems we enforce tenant boundaries at the database and retrieval layers, so tenant separation does not depend on every query in application code remembering a filter.

Integrations you can trust

Third-party integrations get retries, idempotency, and a record of what was synced and when. When an upstream system changes or fails, the platform notices and recovers instead of drifting out of step.

When it is the right fit

A good fit when

An AI feature or prototype works but has no production-grade backend to run on.
You serve multiple customers from one platform and need secure tenant separation.
Data pipelines, background jobs, or integrations are brittle, slow, or hard to debug.

Probably not when

You need a brochure website or simple web app with no data, integration, or scale requirements. A lighter stack and team will serve you better.

Representative technologies

FastAPIDjangoNode.jsNestJSPostgreSQLQueuesCloud deploymentObservability

Backend and Platform Engineering FAQs

Can you build multi-tenant systems?

Yes. Multi-tenancy, access control, and environment separation are core to how we build platforms.

Do you handle integrations with third-party systems?

Yes. Reliable integrations with source tracking and retries are a standard part of the work.

How do you keep the platform observable?

We instrument services for latency, errors, and cost so problems are visible before users feel them.

Can you work with our existing stack?

Usually, yes. We work across Python and TypeScript backends, including FastAPI, Django, Node.js, and NestJS, with PostgreSQL and the major cloud platforms, and extend what you have rather than replace it without a reason.