Technologies
The moment a model is allowed to do something rather than just say something, the back-end stops being plumbing. It becomes the thing deciding what is permitted, what gets logged, and what happens when a step fails halfway through.
From a single API to the whole server estate behind a product, scoped to what you actually need built.
REST and GraphQL interfaces designed around your domain rather than your database tables.
The services an agent calls, built so a wrong call cannot become a wrong outcome.
Deciding what should be one service and what should not, then building it that way.
Scheduled and event-driven work that keeps running when nobody is watching.
Who can do what, enforced server-side rather than hidden in the interface.
Connecting the systems your business already runs on without a migration.
Making an existing back-end hold up, and knowing when it will not.
Engineers who join your team rather than work around it.
The failures that hurt most are rarely outages. They are the quiet ones nobody notices for a month.
Read-only assistants are easy. The risk appears the first time a model is allowed to create an order, issue a refund or update a record, and the guardrails live in a prompt rather than the API.
A third party changes a field, a webhook stops arriving, a token expires overnight. Nothing errors loudly — data just quietly stops being right.
The data model made sense for the first use case. Three features later it is being worked around in every query, and changing it feels impossible.
Chosen for what a project needs, not for novelty. Everything here is something we run in production and can support.
Six stages, each ending in something you can review. No long silences between kickoff and delivery.
01
We map the workloads, integrations and data you already have, and agree what good looks like in measurable terms — latency, throughput and what must never be lost.
02
Service boundaries, data model and API contracts are settled up front, so the interface and the back-end are never built against different assumptions.
03
A thin slice running end to end against real dependencies. It is the cheapest point at which to discover an integration will not behave.
04
Delivered in reviewable increments against the agreed contracts, with tests written alongside the endpoints rather than promised for later.
05
Load testing, failure injection, permission review and a pass over every path where a retry or a partial failure could corrupt state.
06
Deployment, tracing, alerting and a documented handover — then we tune against real traffic instead of guessing at its shape.
We are a software engineering team that works on AI, not an AI team learning to write software.
01
The same team handles the interface and the infrastructure, so the API is never designed against a front-end nobody will build.
02
What a caller may do is enforced server-side. That holds whether the caller is a person, another service, or a model.
03
Actions are logged with their inputs and their outcome, so answering what happened and why is a query rather than an investigation.
04
Types run from the database through the API to the client, so an entire class of runtime bug never reaches production.
05
Critical paths are covered by integration tests against real dependencies. We would rather have twenty tests you trust than a coverage number nobody reads.
06
Your repository, your dependencies, your documentation. No proprietary runtime and nothing that requires us to stay.
Tell us what you are building or what is not holding up in what you have. We will come back with an honest view of the effort involved and where we would start.
