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 behaviour tested against representative cases rather than curated demos alone
- 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. We begin by comparing the proposed AI workflow with simpler retrieval, rules or conventional software approaches and document the trade-offs before selecting one.
Can the model use our internal data?
It may, when the data source, permissions and provider controls fit the use case. We map access and retrieval so the application does not grant a user broader access than the source system allows.
How do you reduce harmful or incorrect outputs?
No team can guarantee that a generative model will never be wrong. We test against agreed cases, constrain data and actions, provide fallback behaviour, and add human review where the consequence of an error is high.
RELATED SERVICES
Often needed alongside this
Custom Software Development
Software built around how your business actually works, rather than a product you have to reshape your operation to fit.
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.
PROOF
We build this for ourselves as well
Products we designed, built and now run ourselves. See the full portfolio.
Tell us the problem, not the brief
We will tell you honestly whether you need AI Products & AI Agents at all, and what a first phase would take. See all services or how we work by industry.