Anyone can get an impressive answer out of a model in an afternoon. Getting the same quality on the ten-thousandth request, with your data, inside your compliance boundary, at a cost you can forecast — that's engineering, and it's the part we do.
It's also the part that changes fastest. So we build behind an abstraction: your retrieval, your prompts, your tool definitions, your evaluations and your guardrails belong to you, and the model sits behind an interface you can swap in an afternoon.
That isn't fence-sitting — we do have opinions about which model suits which job, and we'll give them. It means that when a better or cheaper option appears, changing your mind costs a config change and an evaluation run, not a quarter of re-engineering.
Retrieval index, prompts, tool contracts, eval suite, guardrails, telemetry.
The model, the provider, the hosting location, the inference budget.
Every change is scored against a fixed evaluation set before it ships.
Behind a flag, with the previous behaviour one rollback away.
The unglamorous 80% of quality: chunking that respects document structure, hybrid search, reranking, freshness and permissions enforced at retrieval time — not hoped for in the prompt.
Systems that call your APIs to get work done — with typed tool contracts, hard limits on what can be invoked, and a human approval step wherever an action is expensive or irreversible.
A test suite for a non-deterministic system. Golden datasets from your real cases, automated scoring, regression gates in CI, and honest reporting of what got worse as well as better.
Input and output filtering, PII redaction before data ever leaves your boundary, prompt-injection defences on any content the model reads, and refusal paths that fail closed.
Most failed AI projects are actually failed data projects. We fix the pipeline first: lineage, quality checks, deduplication, and a clear record of what the system was allowed to learn from.
Token accounting per feature and per customer, caching, routing cheap requests to cheap models, and a unit-cost figure you can put next to your pricing page without wincing.
Chosen per workload against your constraints — data residency, latency, cost, and how much of the reasoning has to be right first time. Not a partnership pitch; just where each one tends to fit.
Where we reach when the work involves long documents, careful multi-step reasoning, or agents that have to use tools reliably rather than impressively.
Open-weight models we run inside your own infrastructure, for workloads where the data genuinely cannot leave — or where per-request cost has to approach zero.
Broad ecosystem coverage — multimodal input, realtime voice, embeddings and a large surrounding tooling community that shortens a lot of first drafts.
Model capabilities move month to month. Rather than repeat vendor benchmarks here, we build a small evaluation set from your actual tasks and let it decide. It usually takes a week and settles the argument.
Not "add AI" — one specific, repetitive, high-volume task where being right 90% of the time with a human check is already a win. We'll also tell you when the answer is that you don't need a model at all.
Before any prompt engineering, we collect real examples and define what good looks like. Without this you're tuning by vibes, and vibes don't survive a production incident.
One workflow, one user group, behind a flag, with the human still in the loop. Real usage tells you more in a fortnight than a quarter of design discussion.
Expand scope when the numbers justify it — accuracy, cost per task, time saved. Pull it back when they don't. Both directions are normal.
Describe it in a paragraph. We'll tell you honestly whether it's a good fit, what it would take, and roughly what it would cost to run.