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Capilano AIby Quanteroun Solutions

Approach

From promising prototype to accountable operating system

We treat AI delivery as workflow and operating-model design—not a model demonstration. Every engagement connects value, quality, risk, integration, and ownership.

01

Frame the workflow

Define the user, operating outcome, exception paths, systems, data boundaries, owner, and current baseline.

02

Prove the risky parts

Benchmark extraction, retrieval, conversation, tool use, latency, cost, and human review before scaling the surface area.

03

Build for operations

Add identity, permissions, evaluation gates, telemetry, release controls, escalation, and runbooks from the first production release.

04

Improve with evidence

Review real failures, expand the regression set, measure business outcomes, and grow capability only when the current workflow is stable.

Release standard

A first release should be useful, bounded, and observable

  • Named business owner
  • Representative evaluation set
  • Human approval and escalation
  • Identity and permission controls
  • Quality, latency, and cost telemetry
  • Versioned rollout and rollback

Bring one workflow to a working session

We’ll identify the value, risky assumptions, and most credible first release.