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HAPISTECH

Capability 03

AI and data systems

Retrieval, classification, drafting and analysis applied to your own data, with sources, limits and human review designed in.

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Problem and opportunity

Useful AI is mostly data work and governance

Model access is commodity. The value comes from clean inputs, a defined task, traceable sources and a review step that a responsible person is willing to sign.

  • Knowledge sits in documents nobody can search quickly.
  • Generic assistants answer confidently without citing your material.
  • There is no agreed boundary for what a model may decide alone.

What a governed answer looks like

An answer, its sources, and the person who checks it

Three prepared examples: a question answered from approved documents, a document read into a record, and a question the system should refuse. Refusing is a feature, not a failure.

Prepared example

Which notice period applies to a three-year maintenance contract?

Three months' written notice before the anniversary date, under clause 8.2 of the approved maintenance agreement. An earlier template variant used a different period, so the answer names the document and clause it came from.

Sources attached to the answer

  • Maintenance agreementClause 8.2, approved version
  • Template variants noteInternal summary kept by operations

Before it counts as an outcome: The person answering checks the clause reference before the wording is used with a customer.

Written example, not a live model response. Wording, sources and the review step are set per project.
Text version of this diagram
  • Which notice period applies to a three-year maintenance contract? Three months' written notice before the anniversary date, under clause 8.2 of the approved maintenance agreement. An earlier template variant used a different period, so the answer names the document and clause it came from. Review: The person answering checks the clause reference before the wording is used with a customer.
  • Take the delivery details from this supplier invoice. Supplier, invoice number, delivery date, line items and total are written into the record, each kept with the page it came from. Values the system could not read confidently are left blank and marked for checking rather than guessed. Review: Finance confirms the extracted values against the document before the record is posted.
  • What discount do we give on a five-year support agreement? No approved document covers five-year support pricing, so no answer is produced. The question is passed to the commercial team with its context attached. Review: A person answers. If the answer should be reusable, the approved wording is added to the source material so the next question can be answered from it.

Which documents count as approved, who reviews answers and what the system must never answer are agreed at the start of the project and written down.

What Hapis delivers

Scope of delivery

Data preparation

Collecting, cleaning and structuring the documents and records a system will rely on.

Retrieval and search

Question answering grounded in your own material, with the source shown next to the answer.

Extraction and classification

Turning unstructured documents into structured fields your systems can use.

Review and governance

Confidence handling, escalation paths and logs so decisions can be checked.

How the system works

The layers behind the interface

Each layer has a defined responsibility, so the system can be extended without rewriting what already works.

  1. 01

    Source

    Defined inputs with owners, refresh rules and access boundaries.

  2. 02

    Index

    Chunking, embedding and metadata so retrieval is precise rather than approximate.

  3. 03

    Reason

    Task-specific prompts and tools with explicit limits on scope.

  4. 04

    Verify

    Citations, checks and human approval before anything acts on the result.

Integration and architecture

Where it connects

  • Model access through a gateway so providers can change without a rewrite.
  • Vector and relational storage chosen per workload.
  • Server-side execution only, with keys held in environment configuration.
  • Clear separation between experimental and production behaviour.

Use cases

Typical applications

Internal knowledge assistant

Staff ask questions and receive answers with the underlying document reference.

Document intake

Quotations, specifications or reports parsed into structured records for review.

Analysis support

Drafting and summarising work products that a specialist then verifies.

These describe the shape of the work. Named client references are published only with written approval.

Delivery process

How a project runs

  1. 01

    Discover

    Define the task, the data and the acceptable error profile.

  2. 02

    Design

    Pipeline, evaluation method and review workflow.

  3. 03

    Build

    Iterate against a fixed evaluation set, not one-off demos.

  4. 04

    Integrate

    Connect to the interfaces where the work actually happens.

  5. 05

    Improve

    Monitor quality and adjust as data and usage change.

Questions

Answered directly

Does our data train public models?
We configure systems so client material is used for the task at hand, and we agree data handling in writing before build.
How do you measure quality?
With an evaluation set drawn from your real cases, scored before and after changes.
Can it act automatically?
Only where the risk is understood. Higher-impact actions keep a human approval gate.

Next

Tell us what you need to run better

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