AI that survives contact with real users
Anyone can produce a convincing demo. We build the other eighty percent: retrieval that returns the right document, agents with scoped tools and budgets, evaluation you can trust, and a cost line that does not surprise your CFO.
- Companies whose AI prototype cannot get past a pilot
- SaaS products that need a genuinely useful AI feature rather than a chat bubble
- Operations teams sitting on documents nobody can search
- Businesses worried about AI cost, accuracy or data exposure, and right to be
What you get
LLM applications
Assistants, summarisers, extractors and classifiers grounded in your own data, with citations so a human can check the answer.
Retrieval that works
Chunking, hybrid search, reranking and freshness. Most quality problems blamed on the model are retrieval problems, and they are fixable.
Agents with guardrails
Scoped tools, dry-run and confirm steps for anything irreversible, budgets, timeouts and full tracing. Autonomy proportional to how reversible the action is.
Evaluation harnesses
A graded test set, regression runs on every prompt or model change, and a number you can show a customer instead of a feeling.
Cost and model routing
Per-tenant token metering, cheap models for easy paths, expensive ones only where they earn it, and caching where it is safe.
Privacy and data control
Where data goes, what is retained, what is redacted, and self-hosted or in-region options when your customers require them.
The engagement, step by step
6 stages. Something usable at the end of each one.
- Stage 01
Use-case triage
We rank candidate use cases by value, tolerance for error and reversibility, then say plainly which ones should not use an LLM at all.
- Stage 02
Data and retrieval design
Sources, permissions, chunking strategy, index design and how freshness is maintained.
- Stage 03
Evaluation first
We build the graded test set before the feature, so improvement is measurable from the first day rather than argued about.
- Stage 04
Build and iterate
Prompt and pipeline versioning, structured outputs, fallbacks, and human review where the stakes justify it.
- Stage 05
Harden
Rate limits, budgets, abuse handling, prompt-injection defences, tracing and alerting on quality regression.
- Stage 06
Operate
Ongoing evaluation, model upgrades tested against your suite rather than adopted on release day, and monthly cost review.
What lands in your hands
- 01Production AI feature or standalone AI product
- 02Retrieval pipeline with tuned chunking and reranking
- 03Graded evaluation suite and regression runs
- 04Prompt and pipeline version registry
- 05Per-tenant usage and cost dashboards
- 06Model routing with documented fallbacks
- 07Privacy and data-handling documentation
- 08Human-in-the-loop review workflow where required
Tools we reach for
AI Products & Agents: common questions
Let's find out whether we are the right fit.
A first call is with an engineer, not a salesperson. Bring the problem; we will tell you honestly what we think.