We build AI features that earn their place in a product: assistants that answer from your own documents, automation that removes a genuinely repetitive task, extraction that reads the forms your staff currently retype, and recommendations built into the product, not bolted onto it.
Most AI projects fail in a predictable way. A demo works beautifully on five hand-picked examples, then meets real data and starts confidently inventing answers. Nobody defined what accurate meant, so nobody can tell whether it is working.
We start from the opposite end. Which task, done how many times, costing what today, and what accuracy would make the output trustworthy. Then we build the smallest thing that hits it, with an evaluation set so the number is measured rather than felt.
Sometimes the honest answer is that a rule, a search index, or a better form solves the problem without a model at all. We would rather tell you that than sell you an assistant nobody uses.
AI development means building features on top of machine learning models, usually large language models, so software can handle work that previously needed a person: reading a document, answering a question, drafting a reply, classifying a request, or ranking options.
Most business AI today is integration work, not model training. The model is accessed through an API from a provider such as OpenAI or Anthropic, and the engineering is in everything around it: retrieving the right context, constraining what the model may do, validating the output, and designing what happens when it is wrong.
The key technique is retrieval-augmented generation, or RAG. Instead of hoping a general model knows your business, your documents are indexed and the relevant passages are supplied with each question. The model answers from your material, and can cite it, which is what makes the answer checkable.
The problem it solves is work that is repetitive but not quite mechanical: reading a supplier invoice, answering the same policy question, triaging a support queue. Too varied for rules, too dull to be a good use of a person.
Assistants that answer from your own documents and cite where each answer came from.
Conversational interfaces for customers or staff, with a defined boundary and a route to a human.
Turning invoices, forms, contracts, and scans into structured data your systems can use.
AI inside a process, classifying, routing, summarising, and drafting, instead of sitting in a separate chat window.
Ranking and suggestion built into the product where it affects what people see.
Transcription, summarisation, and analysis of calls and recordings.
Working out what is worth building before any of it is built.
Tasks that are high-volume and low-judgement move to software, and the people doing them move to work that actually needs a person.
Staff and customers get responses from your documentation without waiting for the one person who knows, or for office hours.
Extraction from documents removes a task that is slow, dull, and error-prone, and that nobody has ever enjoyed.
The same question gets the same accurate answer regardless of who is on shift or how busy they are.
Volume can rise without hiring in step, which changes the economics of support and operations.
Semantic search over your own material means institutional knowledge survives people leaving.
With an evaluation set in place you know how often the system is right, which is what makes it safe to rely on.
Plenty of problems presented as AI problems are solved better by a rule, a search index, or a redesigned form. That answer is cheaper for you and we would rather give it.
We agree what correct means and build an evaluation set from your real data. Without that, nobody can tell whether the system works or merely demos well.
RAG architecture keeps responses tied to your documents and shows the source, so users can verify rather than trust blindly.
For consequential decisions the system drafts and a person approves. Full automation is reserved for cases where being wrong is cheap.
Token usage and per-request cost are estimated before build. AI features that turn out to be expensive per call are a common and avoidable surprise.
We are explicit about what leaves your infrastructure, which provider processes it, and what the retention terms are, before anything is sent.
Businesses answering the same queries daily, where an assistant grounded in real documentation removes most of the volume.
Finance, legal, logistics, and insurance teams retyping invoices, forms, and contracts into systems by hand.
Software teams adding search, summarisation, or drafting features that users now expect as standard.
Businesses whose expertise lives in documents, wikis, and long-serving staff, and is hard to reach when needed.
Teams producing hours of recorded calls that nobody has time to review or extract value from.
Retailers and content platforms where recommendation and semantic search directly affect what people find and buy.
An assistant grounded in your documentation, with explicit boundaries and handover to a person when confidence drops.
Extraction with confidence scoring, so clean documents pass through and uncertain ones reach a reviewer instead of failing silently.
Features built into the workflow users already have, rather than a chat window bolted to the corner of the interface.
Semantic search across documents so institutional knowledge does not depend on who happens to still work there.
A short feasibility engagement with a prototype on your real data, which answers the question far more cheaply than a full build.
Where data cannot leave your environment or a jurisdiction, architecture is constrained accordingly. [NEEDS INFORMATION: data residency and privacy requirements that apply to you]
| Deliverable | What it covers |
|---|---|
| Use-case assessment | Written analysis of the task, its volume and current cost, and whether AI is genuinely the right tool. |
| Data review | Assessment of the documents or records available, their quality, and their sensitivity. |
| Accuracy targets | Agreed definition of correct output and the threshold at which the system is trustworthy. |
| Evaluation set | Test cases built from your real data with known answers, used to measure performance objectively. |
| Prototype | A working version on your data, run against the evaluation set before full development is committed. |
| RAG pipeline | Document ingestion, chunking, embedding, and vector search where the use case calls for it. |
| Model integration | Provider selection and integration, with prompt design and output validation. |
| Guardrails | Confidence thresholds, refusal behaviour, output validation, and defined human review points. |
| Cost model | Estimated per-request and monthly running cost at your expected volume. |
| Monitoring & logging | Quality and usage monitoring so accuracy drift is visible instead of surfacing as a complaint. |
| Documentation & handover | Architecture documentation, prompt and configuration notes, and team training. |
AI work is priced in two parts: the build, and the running cost of the models themselves. The second is easy to underestimate and worth modelling before you commit to the first.
What determines the build cost:
Model usage is billed by the provider based on volume, and we estimate it upfront so there are no surprises at scale. A short feasibility engagement is usually the sensible first step, because it answers whether the case holds for a fraction of a full build.
Tell us the task you want automated and we will tell you honestly whether AI is the right tool for it.
There are two costs: building the feature, and running the models. Build cost is driven by use-case complexity, data preparation, accuracy requirements, and integration depth. Model usage is billed by the provider on volume, and we estimate it before you commit.
General models will. That is why we use retrieval-augmented generation, where answers are grounded in your documents and cite their source, along with confidence thresholds and explicit refusal behaviour. The system should say it does not know instead of guessing.
Retrieval-augmented generation indexes your documents and supplies the relevant passages with each question, so the model answers from your material, not from general training. It is what makes answers current, specific, and checkable.
That depends on architecture, and we are explicit about it before anything is sent: which provider processes what, what their retention terms are, and what stays inside your infrastructure. Where data cannot leave your environment, we design for that constraint.
Almost never. For most business use cases, a general model with good retrieval outperforms a fine-tuned one at a fraction of the cost and effort. Fine-tuning is worth considering for narrow, high-volume tasks with abundant labelled examples.
With an evaluation set built from your real data with known correct answers, agreed before development. Accuracy is a measured number reported against a target, not an impression from a demo.
A feasibility engagement with a prototype typically runs two to four weeks. Production features usually take six to twelve weeks, with integration and guardrails taking longer than the model work itself.
Yes. That is the more common request, and the better one: features built into the workflow users already have outperform a chat window added to the side of the interface.
Primarily OpenAI and Anthropic models, with Whisper for speech and open models where data restrictions require self-hosting. Selection follows accuracy needs, cost, and privacy requirements, not preference.
We tell you. Plenty of problems framed as AI problems are better solved by a rule, a search index, or a redesigned form, which is cheaper to build, cheaper to run, and easier to trust. The assessment stage exists to reach that conclusion early.
