Pick the case
One task, with a measurable current cost in time or error rate. A pilot without a baseline cannot be judged.
AI applied to a specific problem in the operation.
Document search, classification, routing, and extraction from messy inputs. Built on your data, evaluated against your cases, and shipped only when it beats the manual process.
Where it earns its place.
The work that suits this is narrow and recognisable. Somebody reads inbound documents and files them. Somebody searches a decade of contracts for one clause. Somebody retypes figures from a supplier PDF into a system that expects them in a different shape.
That work is expensive, repetitive, and slow, and it is the kind of task current models handle well when they are pointed at a defined problem with your data behind them.
The bar is measurable. A tool goes live when it beats the manual process on accuracy and time against a set of your own cases, and it stays measured after launch.
What we build.
Systems built around your documents and records, with retrieval over your own content rather than a general model answering from memory. Outputs cite the source they came from, and cases the model is unsure about route to a person instead of going out unchecked.
Includes
- Document search and retrieval over internal content
- Classification and routing of inbound records
- Extraction from unstructured and scanned sources
- Internal knowledge and support tooling
- Evaluation sets and accuracy monitoring
- Model routing and cost control
How the work runs.
Build the evaluation set
Your real cases, with the right answer recorded, before any model is chosen. This is what makes the result arguable rather than impressive.
Build and measure
Retrieval, prompting, and routing tuned against that set. Cheaper models are used wherever they hold the accuracy bar.
Ship with a fallback
Low-confidence cases go to a person. Accuracy and spend stay monitored after launch.
Questions we get asked.
Does our data get used to train somebody's model?
Not under the arrangements we build on. Enterprise API terms from the major providers exclude training on submitted data, and where content is sensitive enough to warrant it, models can run inside your own environment. Which applies is agreed before anything is sent anywhere.
What does it cost to run?
Model usage is metered, so the running cost is estimated from your volumes before the build and monitored after it. Routing simpler cases to cheaper models is usually the difference between a sensible bill and a surprising one.
How accurate is it?
That is measured against your cases rather than quoted from a benchmark. The number that matters is accuracy on your documents compared with the people doing the task today, and the tool ships only if it wins.
What if it gets something wrong?
Confidence thresholds route uncertain cases to a person, and outputs cite the source they came from, so a wrong answer is traceable. For anything with financial or legal effect, a person stays in the loop by design.
Start with the problem.
Describe what is slowing the operation down. We will come back with the approach, the scope, and what it costs.
Get in touch