Practical AI for one workflow at a time

Start with a task worth improving. Map the work, review the data and test a focused solution before expanding it across your business.

Illustration of documents flowing through an AI processing step into a reviewed table and a checked, finished document

What is covered

6 areas. We scope the ones you need, not a bundle you do not.

Readiness and workflow assessment

Identify a practical first use case by reviewing one workflow, its data, integration needs and the people who use it.

  • Workflow mapping with process owners
  • Data quality and access review
  • Build-versus-buy and AI suitability assessment
  • Pilot scope, measurement plan and cost estimate

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Workflow automation and agents

Connect approved tools and automate defined steps such as intake, classification, drafting and routing, with review where a person should decide.

  • Request classification and task routing
  • Draft generation for staff review
  • CRM, email and API integrations
  • Approval steps, run logs and failure handling

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Knowledge assistants

Make approved business knowledge easier to find with an assistant that cites its sources and respects agreed access rules.

  • Internal document search with cited answers
  • Support assistant using approved reference content
  • Knowledge source preparation and update workflows
  • Answer evaluation and human escalation

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Document processing and data extraction

Turn recurring documents into structured records, with validation and a review queue for uncertain results.

Paper document compared with extracted data fields on a laptop for document processing
  • Document intake and classification
  • Field extraction from agreed document types
  • Validation rules and exception review
  • Export to approved business systems

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AI infrastructure and deployment

Plan and deploy what runs behind an AI solution, with hosting, access, data flows and operating costs documented.

Compact computing workstation in a small office for hosting AI infrastructure
  • Cloud or self-hosted architecture assessment
  • Containerized application and retrieval service deployment
  • Secrets, access and operational logging setup
  • Capacity, backup and infrastructure cost planning

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Operations, training and improvement

Keep a workflow useful after launch with quality checks, usage monitoring, team guidance and a clear maintenance scope.

  • Availability, error and usage monitoring
  • Evaluation after model or source changes
  • Team training and practical usage guidance
  • Scheduled review of outcomes and running costs

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How it works

  1. Choose a useful workflow

    Identify a recurring task and record how it is handled today.

  2. Agree on data and success criteria

    Define permitted data, access, review steps, running costs and measurable acceptance criteria.

  3. Build and test a pilot

    Connect the agreed tools and test outputs on a representative sample before production use.

  4. Roll out and maintain

    Train users, monitor outcomes and costs, and agree who is responsible for ongoing changes.

How pricing works

Begin with a defined assessment or pilot. The proposal separates setup and implementation from model usage, hosting, licences and ongoing support. No savings percentage or fixed return is promised before testing.

Frequently asked questions

Do we need a custom model?

Not always. Existing models, document retrieval or conventional automation may fit better. The assessment compares approaches before a build is proposed.

Will our data leave our systems?

That depends on the architecture and the model provider. Data flows, hosting, retention and external services are documented and agreed before deployment.

Will AI act without a person checking it?

The workflow defines which steps may run automatically and which need review. Actions such as sending customer messages or changing business records get an agreed approval design.

How much does an AI project cost?

Costs depend on the workflow, data preparation, integrations, testing and hosting. Assessment, implementation, model usage and ongoing maintenance are scoped separately.

Tell us which repetitive task you want to improve, where the data lives and what a useful result would look like.

Share what you have, even if it is rough. We will tell you what we would need to scope it.