Services / AI automation

AI automation for business workflows.

We’re a UK AI automation agency based in Bradford, building document and enquiry workflows around your existing systems. Our processes extract document data, categorise enquiries and prepare drafts for review, with checks before updating records or sending information.
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For work that starts with documents, messages or free text.

AI can help when the input varies too much for a simple rule: different document layouts, customer descriptions or long email threads. We start with examples from the task, establish how results will be checked and compare the approach with ordinary automation.

Problems this work can address

  • People read documents and enter the same fields into a system.
  • Enquiries need to be categorised before the right team can respond.
  • Staff prepare similar replies using information from several records.
  • You have an AI prototype but no reliable way to assess its results.

Scope

What we can build with you.

We establish which parts are useful for your business and what your existing tools can already do before agreeing the build.

  • Document data extraction

    Extract agreed fields from PDFs, emails or attachments. Validate required information and send missing or uncertain results for review before they become business records.

  • Enquiry classification

    Categorise messages against an agreed set of topics or teams. Test on representative examples and provide a route for messages that do not fit.

  • Draft replies and summaries

    Prepare a draft using the relevant source information, with a review step where needed. Show the material used so the reviewer can check the result.

  • Connections to existing workflows

    Pass approved results to a CRM, database or task system. Defined rules control what the AI can change and what needs a person’s decision.

  • Evaluation and monitoring

    Create a set of example inputs and expected results. Test changes against it and record failures, review effort and running costs.

  • Data handling and maintenance

    Agree which information can be sent to the chosen provider, who has access and what is retained. Document prompts, review steps and maintenance responsibilities.
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In practice · Related integration work · Recruitment

Connecting business software and custom applications.

The recruitment case shows our work connecting a CRM, Airtable, n8n and custom applications. It demonstrates integration experience; it does not report AI performance or outcomes.

Read the case study

Test a specific task. Decide whether the results justify a rollout.

A useful trial needs real examples, an agreed definition of an acceptable result and a way to handle mistakes. We establish those before connecting AI to a live business process.

  1. Scope

    Assess the task, data constraints and alternatives.

    Your involvement: Provide sample inputs and explain how they are handled today.

    Trial scope and success criteria

  2. Build

    Build a trial with validation, review steps and logging.

    Your involvement: Review example results and flag mistakes.

    Prototype and evaluation examples

  3. Test

    Test varied inputs, failures and expected running costs.

    Your involvement: Assess accuracy and the time spent checking results.

    Results and rollout recommendation

  4. Launch

    Connect the agreed workflow and hand over monitoring.

    Your involvement: Approve the process and assign reviewers.

    Controlled rollout and support plan

After launch

We agree launch aftercare, handover and any ongoing maintenance or development with you. Your organisation retains access to its code, accounts and documentation.

Support and ongoing development

Planning the work.

Bring one repeatable task and examples of good results.

We will review the inputs, the decisions involved and what happens if the output is wrong. From that, we can propose a limited trial and the checks needed to decide whether to use it.
When is ordinary automation a better choice?

When the input is structured and the rules are clear, a standard integration is usually easier to test and maintain. AI is worth assessing where documents or language vary and simple rules cannot reliably handle them.

How do you measure whether the AI is useful?

We test against agreed examples and inspect different types of error. We also assess the time people spend reviewing results and the cost of running the process. Accuracy alone is not enough if checking the output takes as long as the original task.

Will AI send messages or change records automatically?

Only within the rules agreed for the project. Drafts and extracted information can require approval before anything is sent or updated. We define permissions, validation and the response to failures before rollout.

What determines cost and which data can be used?

Document volume, model usage, integrations, review requirements and monitoring affect cost. Data requirements also influence the provider and setup. We review those with your team before using live information and identify recurring provider charges separately.

How can we help you work better?

You don’t need a brief or a solution in mind. Tell us what’s proving difficult or what you’d like to improve. We’ll explore the options together and agree where to start.

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