Custom AI Projects

AI automations for document workflows that need traceability.

When the problem does not fit Liravo or Simply HS, we design a bounded pilot to validate whether an internal agent or AI workflow can operate safely.

Diagnose a custom AI projectBack to home

Operating principle

First we test the workflow; then we decide whether it deserves to operate.

Who it is for

Teams with repetitive document review
Operations that need research, synthesis, or sourced reporting
Areas losing follow-up across email, Excel, folders, and systems
Companies that want a realistic pilot before buying or building a platform

Problem it solves

  • The process depends on manual reading, repetitive search, or hand-built reports.
  • The information exists, but is spread across documents, emails, and systems.
  • The team needs to prepare decisions, not have AI decide for them.
  • The automation idea still needs scope, data, risks, and acceptance criteria.

Candidate workflow

  1. 1

    Use Guided Discovery to structure the problem and operational context.

  2. 2

    Define available data, systems, owners, and human approval points.

  3. 3

    Choose a small sample to validate the workflow with real evidence.

  4. 4

    Build an agent or workflow that prepares, searches, summarizes, alerts, or drafts.

  5. 5

    Measure results and decide whether to expand, operate, or stop.

What it delivers

  • Problem brief and pilot scope.
  • Current workflow map and human decision points.
  • Measurable prototype or workflow with a real sample.
  • Acceptance criteria, risks, and data needs.
  • Next-step recommendation: operate, iterate, or stop.

Important limits

  • We do not promise total automation or guaranteed results.
  • We do not hide critical decisions inside the agent.
  • We do not deploy production without validating scope, data, and human review.
  • We do not treat research or synthesis as final truth without reviewable sources.