Services

Productized advisory services for AI workflows

Each engagement is built around one practical question: which workflow should use AI, how should the pilot be designed, and what needs to be true before it becomes daily operations.

AI Workflow Diagnostic

A two-week diagnostic to identify which workflow is worth improving with AI and what risks must be handled first.

Best for
Teams with many AI ideas but no clear first workflow.
Deliverables
  • Workflow inventory
  • AI fit score
  • Risk and review recommendations
  • First pilot recommendation
Timeline
About 2 weeks
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Pilot Design Sprint

Turn one promising workflow into a testable AI pilot, not just a demo.

Best for
Teams that know the workflow but need scope, metrics, and human-AI handoffs designed.
Deliverables
  • Pilot scope
  • Human-AI workflow
  • Success metrics
  • Launch and feedback plan
Timeline
About 4-6 weeks
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AI Workflow Implementation

Implement one AI-assisted workflow with source material, review points, interface, and feedback loops.

Best for
Teams ready to put a designed AI workflow into real daily work.
Deliverables
  • AI workflow build
  • Tool and system integration
  • Review and exception handling
  • Pilot rollout support
Timeline
About 8-12 weeks
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AI Operating Model Review

Review an AI workflow that already exists but is stuck on quality, adoption, ownership, or scale.

Best for
Teams with an AI tool or pilot that is not yet producing reliable operational value.
Deliverables
  • Workflow health check
  • Ownership and governance recommendations
  • Quality monitoring approach
  • Scale, adjust, or stop decision
Timeline
About 2-3 weeks
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Where you are now

Choose based on where the work is stuck

Support pressure

Use AI Customer Support when teams are overwhelmed by repetitive requests.

Scattered knowledge

Use AI Knowledge Systems when information exists but is hard to retrieve or trust.

Manual operations

Use AI Workflow Automation when repetitive work slows your team down.

How the offers connect

Supporting capabilities

  • Custom integration and API work
  • Internal tools and workflow interfaces
  • Content and data ingestion pipelines
  • Operational rollout and iteration