AI Development
AI features that do a specific job: answering from your documentation, triaging incoming work, extracting structured data from messy inputs, automating a process a person currently repeats. Scoped around a task, evaluated against real examples.
The problem this solves
There is enormous pressure to "add AI" and very little clarity about where it belongs. The result is usually a chatbot bolted onto a homepage that answers worse than the FAQ page it sits above.
Useful AI work looks different: it targets a specific task where the input is messy or the volume is high, and it is measured against whether that task got faster or better.
What we build
- Retrieval assistants — answering from your documentation, policies, catalogue or knowledge base, with sources attached
- Back-office automation — classifying, routing, summarising and extracting structured data from email, forms, documents and tickets
- In-product AI features — search, drafting, tagging and recommendations inside software you already run
- Internal tools — the assistant a team uses to do a repetitive job in a fraction of the time
How we approach it
Start from the task. What does a person do today, how often, and what makes it slow? That defines both the design and the measure of success.
Ground it in your data. Retrieval over your own content, so answers come from documents you control and can point at.
Evaluate before launch. A set of real examples with expected outcomes, run against every change. Without it, "it seems better" is the only available verdict.
Constrain and cost it. Clear boundaries on what the system may do, what it must refuse, and how much it may spend.
Technologies
OpenAI and Anthropic APIs, vector search over your content, and the same Next.js and Node stack as the rest of our work — because an AI feature is mostly ordinary software with a model somewhere in the middle.
What you get
A working feature in your own infrastructure with keys in your accounts, an evaluation set you can re-run, documented prompts and retrieval logic, cost reporting, and a written account of what the system is and is not reliable at.
Who this is for
Teams drowning in repetitive language work, businesses with documentation their customers cannot navigate, and software products where a model would genuinely improve a specific screen.
Related services
Most AI work ships inside an existing product, which makes it software development with a model attached. If the AI feature is the product, SaaS development covers the tenancy and billing around it. And if you want AI assistants to cite your company at all, that is an SEO problem — see how to get cited in AI answers.
Frequently asked questions
- Does my business actually need AI?
- Often not. If a problem is a rules problem, a database query or a better form, a language model makes it slower and less reliable. We will tell you that before quoting. AI earns its place where inputs are messy, language-shaped, or too varied for rules.
- Will it make things up?
- Any language model can. The mitigation is architectural: ground answers in your own documents, cite the source alongside the answer, constrain what the system is allowed to do, and evaluate against real examples before launch rather than after complaints.
- What does it cost to run?
- Model usage is billed per token, so running cost scales with traffic. We size it during scoping, cache aggressively, and pick the smallest model that passes evaluation — the difference between a considered and a careless implementation is often an order of magnitude.
What is included
- Retrieval assistants over your own content
- Workflow and back-office automation
- LLM integrations inside existing products
- Evaluation, guardrails and cost control
Tell us what you are building.
Send us the problem — an idea, an existing product, a site that is not performing. We will tell you what is worth building, what is worth fixing, and what it costs, before anyone signs anything.