THE PROBLEM
There is pressure to use AI, and no shortage of demonstrations. Far fewer of them survive contact with real users, real data and a real cost model.
What we usually hear- Impressive prototypes that fail on the edge cases the business actually cares about
- No way to tell whether output quality is improving or regressing
- Inference costs that scale in a way nobody modelled
- Data scattered across systems the model cannot reach
HOW WE APPROACH IT
Engineering, not guesswork
We start by asking whether the problem needs AI at all. Retrieval, rules or a well-designed form often solve it more reliably and more cheaply, and we will say so.
Where AI is right, we build the unglamorous parts properly: the data pipeline, the retrieval layer, the evaluation harness and the guardrails. Those decide whether it works in production.
Agents get explicit boundaries, fallback behaviour and human review points, so failure is contained rather than silent.
WHAT YOU RECEIVE
Deliverables
- Use-case assessment, including an honest view on whether AI is the right answer
- Retrieval and data pipeline over your own content
- Agent design with guardrails, evaluation and fallback behaviour
- Cost modelling and monitoring for inference at scale
WHAT CHANGES
Business outcomes
- AI features that hold up on real data rather than curated demos
- Output quality measurable, so changes can be judged
- Inference cost modelled and monitored
- Clear boundaries on what the system is allowed to do unattended
TECHNOLOGY
What we typically build with
Selected against your constraints and your team's ability to maintain it — not against fashion.
- OpenAI
- Anthropic
- Gemini
- LangChain
- Python
- TypeScript
- PostgreSQL
- pgvector
HOW WE DELIVER IT
The same five phases, every time
See the full twelve-stage process for what each phase produces.
Discovery
We map the business problem and define success in measurable terms.
Architecture
Technology and data decisions made against your constraints, documented with reasoning.
Design
Journeys and interfaces resolved on screen, including everything that goes wrong.
Engineering
Short cycles ending in working software, reviewed and tested before it merges.
Deploy & Scale
Automated release, monitoring in production, and support as usage grows.
QUESTIONS
About ai products & ai agents
Will you tell us if AI is the wrong answer?
Yes, and we often do. A retrieval system or a rules engine is frequently more reliable and cheaper for the problem described.
Can the model use our internal data?
Yes, through a retrieval layer over your own content, with access controls respected so users only ever see what they are entitled to.
How do you stop it producing wrong answers?
Grounding in your data, evaluation against cases you define as correct, guardrails on what it can act on, and human review where the cost of being wrong is high.
RELATED SERVICES
Often needed alongside this
Business Process Automation
Replace the manual work your team repeats every week with systems that run themselves and report when they cannot.
API Development & Integration
The interfaces between systems, where most project estimates go wrong and most production incidents begin.
Product Engineering
A long-running team that owns discovery, delivery and iteration — for products that keep evolving after launch.
Tell us the problem, not the brief
We will tell you whether ai products & ai agents is actually the right answer, and what a first phase would take. See all services or how we work by industry.