AI solutions: development and integrations
We build the unglamorous half of applied AI — the retrieval, the evaluation harness, the queues and the fallbacks — so a model becomes a feature your business can depend on rather than a demo.
Three situations where a call with us is worth an hour
A model works in a notebook, not in production
Prompts live in someone’s laptop, output quality drifts, and nothing is versioned. We put it behind an interface with tests, logs and a rollback path.
Retrieval returns plausible nonsense
Chunking, embeddings and ranking are guesses. We rebuild retrieval around your actual documents and measure answer quality before shipping.
The AI part has to reach into legacy systems
The value sits behind a Rails monolith, an ERP or an unversioned API. Integration is the project; the model is one component of it.
Applied AI is a systems problem. Most of the work is data access, evaluation and failure handling — the parts that decide whether anyone trusts the output six months later.
We start from the decision, not the model
Before choosing an approach we ask what action the output triggers and what it costs when the answer is wrong. A misfiled invoice, a wrong triage suggestion and a slightly worse email subject line deserve entirely different levels of caution, and that single question usually settles the architecture.
That framing keeps scope honest. It is common for us to conclude that half of what was planned as “AI” is deterministic work — parsing, matching, a lookup table — and to spend the model budget only where judgement is genuinely required.
Evaluation before features
We build a labelled set from your own data and a harness that scores every change, so prompt edits, model swaps and retrieval tweaks are compared rather than argued about. Regressions show up in the pipeline, not in a support ticket.
The harness stays yours. It runs in CI next to the rest of the test suite, and it is the thing that lets a new provider or a cheaper model be adopted in a week instead of a quarter.
Integration is where the risk lives
We wire models into the systems of record you already have — the Rails app, the queue, the warehouse, the third-party API with the 30-second timeout. That means idempotent jobs, backpressure, caching, per-tenant limits, and a defined behaviour for every failure the provider can hand back.
Cost and latency are treated as product requirements. We instrument tokens per request and time to first byte from the first week, because both of them shape what the feature is allowed to be.
If a model’s answer cannot be traced, measured and switched off, it is not in production — it is on loan.
Work like this usually touches
Have an AI feature that needs to survive contact with real data?
Send the context you have — a repo, a diagram, or three paragraphs of frustration. We reply within one business day.