AI implementation
AI implementation for real business processes
Most AI projects do not fail because the model is weak. They fail because nobody agreed what a correct answer looks like, so there is no way to tell whether the thing works. We start at the other end: one narrow task, a set of real examples, a threshold it has to beat, and only then an integration into the system your team already uses.
Narrow problems, measured output, your data staying yours
Document and data extraction
Invoices, delivery notes, statements, applications and contracts turned into structured records that land in your system - with the low-confidence cases routed to a person instead of guessed at.
Retrieval over your own content
Answers drawn from your policies, manuals, specifications and historical records, each one citing the document it came from so a reader can verify it rather than trust it.
Assisted workflows
Drafting, classification, summarising and triage placed inside the screens people already work in, with a human approving before anything is committed or sent.
Evaluation and guardrails
A held-back test set built from your real data, measured accuracy against it, a known cost per run, and defined behaviour for when the model is wrong or the provider is down.
You probably need this if…
- Staff retype the same information out of PDFs and emails every day
- Knowledge that matters lives in documents nobody can search
- You have been quoted for an AI pilot and cannot tell what it would do
- A vendor demo worked perfectly and then fell over on your real data
What you get
- A single defined use case with a measurable success threshold
- An evaluation set built from your own documents
- Working integration inside your existing system
- Accuracy, latency and cost-per-run benchmarks
- Documented fallback behaviour and human review steps
Everything we produce is yours, including the discovery document - whether or not the build goes ahead.
AI implementation: common questions
Does our data get used to train a vendor model?
Not on any configuration we deploy. We use providers whose commercial terms exclude training on submitted data, or we run a model on infrastructure you control, and we tell you in writing which applies before anything is sent anywhere.
How do we know it is accurate enough to rely on?
Because we measure it. Before the build we agree the task and set aside real examples with known correct answers. The result is a number you can argue with, and anything below the agreed threshold goes to a person instead of straight through.
AI implementation: tell us what you need
Send a short description of the problem. You will get a straight answer on whether this is the right fit and what it would take.
A reply from a developer. Not a sales pipeline. The person who reads your message is the person who would do the work.
An honest answer on fit. If an off-the-shelf product solves your problem better than a custom build, we will tell you which one.
No obligation. A first conversation costs nothing and commits you to nothing. Discovery output is yours either way.