AI Integration & Automation
The same capability booked as a defined service, end to end.
Specialized AI Talent
AI Engineering Capacity for
Applications in Production
Practical AI capability for teams whose application already exists — integration work inside your stack, not a research project beside it.
AI Capability Inside an Existing Application
The AI work that reaches production is mostly integration work. The interesting part is rarely the model call; it is what happens either side of it — what content the answer is drawn from, how the result is checked, what happens when it is wrong and where a human gets to review before anything is committed.
That is why this capacity sits with the engineers who already work on your application. Retrieval needs your data model. Summarisation needs your permissions. A drafted reply to a customer needs your audit trail. Adding an AI feature to a live product means touching the same services, the same identity system and the same deployment pipeline as everything else.
We do not develop foundation models, conduct model research or position ourselves as an AI research lab. What we do is make AI capability usable inside software that people rely on at work, with the review and fallback behaviour that requires.
What This Capacity Covers
Engineering tasks that turn an AI capability into something a team can use at work.
- Retrieval over your own documents and records
- Search and question-answering inside an application
- Document extraction and classification workflows
- Drafting and summarisation with a review step
- Model API integration behind your own service layer
- Prompt, context and output evaluation
- Guardrails, fallbacks and safe failure behaviour
- Logging and cost monitoring of AI usage
- Feedback capture so quality can be judged over time
Every item above is software engineering with an AI component in it — which is why it suits teams with an existing application rather than a blank sheet.
Where Teams Ask for AI Capacity
The common thread is an application that is already trusted with real work, and a feature that needs careful engineering around it.
- A backlog item has sat unstarted because no one has done it before
- A proof of concept works on a laptop and has no path to production
- An AI feature needs to respect existing permissions and audit rules
- Documents are being read by people that software could triage first
- Support or operations queues are absorbing repetitive reading
- Someone needs to own accuracy, cost and review behaviour as the feature matures
How We Work Alongside Your Team
An AI feature does not get to be a special case in the codebase. It gets reviewed like any other change: tested, observable, reversible, with a documented failure mode. That is the standard the capacity works to.
Evaluation is treated as part of the build rather than a one-off demonstration. Before a feature is exposed, we agree what a good answer looks like on a sample of real content, how that gets checked as things change and who sees the result when it is not good enough.
Where a requirement genuinely calls for research rather than engineering — training, fine-tuning at scale, model development — we say so. Most product needs do not go there.
Data, Rights and Caution
Anything that reads your content has to respect what your application already respects: who can see which record, what may leave the system and what must be traceable afterwards.
Where information is sensitive, the sensible first step is usually a narrow, verifiable workflow rather than a broad assistant, and we will recommend the smaller one.
Not a Research Programme
This capacity is for teams who want an AI capability shipped in an application they maintain, with the ordinary engineering that makes it dependable.
No model training, no benchmark theatre, no claims about accuracy that no one has measured on your content.
What to Tell Us
The specific workflow is more useful than an ambition.
None of this is essential to get in touch. It simply means the first reply can be more useful than a request for more information.
Useful to include
- Which part of the application the feature belongs to
- What content it would read, and in what volume
- Who reviews the output, and what happens if it is wrong
- Whether anything is already built, or tried and abandoned
- Constraints around data location, privacy or audit
Related Work
AI capacity normally arrives alongside application work, not instead of it.
ASP.NET Core
The backend layer AI features usually land in: services, APIs, background jobs.
Dedicated Engineers
Broader ongoing capacity when AI work is one part of a bigger backlog.
Business Systems & Integrations
Connecting the systems the AI feature has to read from and write to.
Have a Software Project in Mind?
Whether you're building something new, modernising an existing application or need specialist development support, let's discuss what you need.