We build AI that works on your data, not the demo dataset — evaluated, observable, and cost-tuned to run in production without burning a series A on tokens.
Not a menu of buzzwords — the concrete things our team delivers on every ai development engagement.
Hybrid search, re-ranking, and evals on your corpus — not a five-line LangChain example from a blog.
pgvector, Pinecone, or Weaviate paired with Postgres so your AI respects ACLs and business rules.
We benchmark prompt, RAG, and fine-tune options before recommending one. Most of the time you do not need to fine-tune.
LangSmith, Langfuse, or custom evals wired in from day one. No "it worked in testing" surprises.
Prompt injection defences, output filters, and PII redaction built for enterprise review.
Swap GPT-4o, Claude, Gemini, or open models via a single abstraction. No vendor lock-in.
No discovery phase that never ends. Each step has a deliverable, a date, and a demo.
We look at your data, sample queries, and current pain points before promising an AI solution.
A working prototype on your data in two weeks, with an eval set and a baseline accuracy number.
Caching, streaming, cost controls, and fallback models before the first user sees it.
LangSmith or Langfuse dashboards, alerting on drift and cost, and a weekly eval review cadence.
Opinionated defaults — not a buzzword bingo card. We swap pieces when your product calls for it.
Most of the work is ordinary software engineering. A realistic split on an AI feature is roughly 20% model and prompt work, 80% data plumbing, evaluation, observability, cost control and the product surface around it. Teams that staff only ML specialists produce impressive notebooks and no shipped feature; teams that staff only product engineers ship something that cannot be measured or safely changed.
The single artefact that separates AI projects that survive from ones that freeze is an evaluation harness — a golden set of real historical cases with expected outcomes. Without it you cannot upgrade a model, change a prompt, or prove to anyone that quality has not regressed. We build the harness before the feature.
Use an API. For the overwhelming majority of products, a hosted frontier model with good retrieval and a tight evaluation loop beats anything you can train, and it costs a fraction. Training your own model makes sense in a narrow set of cases: you have proprietary data at real scale, latency or unit economics rule out hosted inference, or data residency forbids sending anything to a third party.
Fine-tuning sits between the two and is frequently reached for too early. It changes how a model behaves, not what it knows. If the problem is that the model lacks your facts, retrieval solves it in days; fine-tuning will not, and teams routinely spend six weeks discovering that.
Instrument cost per request from the first week, broken down by feature rather than as one monthly bill. Most teams discover one endpoint accounts for most of the spend, and it is usually a retrieval step pulling far more context than the model needs. Trimming retrieved context, caching embeddings, and routing easy requests to a smaller model typically cuts inference cost 40-70% with no measurable quality change.
Set hard ceilings per request and per day before launch. A model stuck in a reasoning loop is a cost incident before it is a correctness incident, and the ceiling is what turns a bad night into a caught alert.
Constrain the output shape, ground every factual claim in retrieved source data, cite it so a human can verify, and put a person in the loop on anything consequential. In healthtech and financial services that last point is not optional — regulators consistently expect a human checkpoint on decisions that affect someone materially.
Log the prompt, the retrieved context and the response for every call, so a bad output can be reproduced rather than argued about. Teams that skip this cannot investigate their own incidents.
A 30-minute call. We'll talk scope, timelines, and what a realistic first release looks like. NDA signed before we start.