AI Solutions

AI applied to your data, not to a demo.

Most AI value does not come from replacing software. It comes from adding retrieval, extraction and decision-making to the systems that already hold your information — behind the interfaces your team already opens.

Where this starts

Not with a model choice. With the question your team asks most often, the document they retype most often, and the decision that waits longest for a person.

What an AI solution actually is here

An AI solution is a production system with a model inside it, and the model is the smallest part. Around it sits the ingestion that gets your data in, the retrieval that finds the right passage, the schema that forces an answer into a usable shape, the evaluation that proves it works on your cases, and the logging that lets you audit it afterwards.

Leave any of those out and you have a demo — convincing in a meeting, unreliable in the first week it meets real inputs.

Retrieval over your own documents

Contracts, policies, manuals, tickets, spreadsheets, scanned PDFs. We build the pipeline that turns them into a searchable corpus: chunked so that a passage still makes sense on its own, kept current as documents change, and scoped so a question only reaches the documents the person asking is allowed to see.

Every answer cites the passage it came from. Where the corpus does not contain the answer, the system says so rather than producing a plausible one — a behaviour we write tests for, not one we hope for.

Extraction from documents that were never structured

Invoices, statements, forms and scans become records with fields. Confidence is scored per field: low-confidence values route to a person with the source image beside them, and the values the system is sure about post automatically.

Accuracy is measured against a labelled set of your own documents before anything is switched on — including the bad scans, which is exactly where extraction usually fails and rarely gets tested.

AI integration into what you already run

You rarely need to replace anything. Your CRM, ERP, ticketing system or internal platform stays where it is, and we add capability to it through its APIs — a drafted reply, an enriched record, a classified case, a summarised thread — appearing inside the tool your team already uses.

This also keeps the rollout reversible. An integration can be switched off and the business keeps working the way it did last week, which is what makes it possible to start small.

In practice

You rarely need to replace anything. Most value comes from adding retrieval, extraction or decision-making to software that already holds your data — behind the interfaces your team already uses.

What a build includes

  • API integration
  • Retrieval over your data
  • Model selection & cost control
  • Evaluation harness
  • Staged rollout
Our position

What we will tell you before you spend anything

Three things decide whether an AI solution is worth building at all.

  • Your data has to be reachable

    If the information lives only in someone's inbox, or in a system with no export, that is the first project — and it is not an AI project.

  • An answer needs a right answer

    We can only evaluate what has a ground truth. Where correctness is genuinely contested, the system's job is to present the evidence, not to decide.

  • A cheaper mechanism usually exists

    A query, a rule or a better-designed form beats a model on cost, speed and auditability. We will say so, and lose the scope.

Related

Have data you cannot get answers out of?

Tell us what the question is and where the information lives. We will tell you whether retrieval, extraction or neither is the right shape.

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