Useful, not impressive.
AI Development & Integration Agency
AI integrated where it removes real work, scoped to a measurable workflow improvement rather than added as a headline feature.
You are probably here because
- A team is spending hours on work that is pattern-matching, not judgement.
- You have a pilot that demos well and has never survived real usage.
- Your product needs an AI feature that holds up to customers, not a demo.
- You want to know what is genuinely worth automating before committing budget.
Where AI actually earns its keep.
Internal assistants & copilots
Assistants grounded in your own documents, policies and data, with the retrieval and evaluation work that separates something a team trusts from something they quietly stop opening.
Document & data pipelines
Extraction, classification and summarisation over the intake nobody wants to do by hand — forms, invoices, tickets, transcripts — with confidence thresholds and a human step where the cost of being wrong is real.
AI features inside your product
Customer-facing capability built to production standards: latency budgets, cost per request, graceful failure, and behaviour that stays sane when users do something unexpected.
Evaluation & guardrails
Test sets, regression checks and monitoring, so you can tell whether a model or prompt change made things better rather than just different.
What you actually get
Every engagement ends with these in your hands, owned outright.
- A scoped workflow with a measurable baseline to improve on
- Working integration inside the tools your team already uses
- Evaluation set and regression tests for output quality
- Cost and latency budgets, monitored rather than assumed
- Fallback behaviour for when the model is wrong or unavailable
- Documentation covering what it does and, importantly, what it does not
How we work
Architect
We map the system before a line of code: data model, surfaces, and the constraints that actually matter.
Build
Engineered in tight, reviewable increments. You see working software early and often, not at the end.
Harden
Performance, security, and observability baked in before launch, not patched after the incident.
Scale
We instrument what matters and iterate on the parts that move the business, release after release.
Related work
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Read the case study →Questions, answered
/ 01How do we know AI is the right solution and not just a rule?+
Often it is not, and we will say so. If the task has stable rules, a rule engine is cheaper, faster and easier to audit. AI earns its place where inputs are messy and variable — free text, mixed document formats, natural language — and where being approximately right at speed beats being exactly right slowly.
/ 02What does an AI project cost to run, not just to build?+
Running cost is the part most pilots ignore, and it is why many never reach production. We budget per request from the start — model choice, token volume, caching, and how much can be handled by cheaper models or plain code — so the unit economics are visible before you commit to rollout.
/ 03Where does our data go?+
That is a scoping decision we make with you rather than a default we impose. Depending on sensitivity, workloads can run through providers with zero-retention terms, in your own cloud, or on models you host. We document what leaves your environment and what does not.
/ 04Can you improve an AI feature we already built?+
Yes, and it is a common starting point. Most stalled pilots fail on retrieval quality, evaluation or error handling rather than on model choice. We audit which of those is actually the problem before recommending anything be rebuilt.
Our thinking on Tech Solutions.
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Let's build an unfair technical advantage.
Part of Tech Solutions.