Ownership is split across teams
Business, data, platform, security, and application owners each see part of the service but nobody owns the outcome end to end.
Managed AI & Automation Operations
Keep AI agents and automations reliable after the launch team moves on
Operate AI agents and automations after launch with monitoring, incident response, quality reviews, cost controls, change management, and continuous improvement.
- Best suited for
- Organizations with deployed agents and automations that need accountable operations
- Engagement shape
- Ongoing monitoring, governance, support, and improvement
The service in practice
Start with the work that needs to improve
AI-enabled workflows change more often than conventional software: models, prompts, knowledge, integrations, policies, and user behavior all affect the result. Someone must own the complete service after release.
Capilano AI provides managed operations for agents and automations, combining service monitoring, quality review, incident response, controlled change, cost visibility, documentation, and continuous improvement.
Why this work matters
Replace operational friction with a service your team can trust
What is getting in the way
Quality failures do not look like outages
The system can remain available while retrieval, tool use, classification, or user outcomes quietly deteriorate.
Changes bypass release discipline
Prompt, model, connector, source, and policy changes can alter behavior without versioning, evaluation, approval, or rollback.
What the engagement should change
An accountable service model
Inventory, owners, service expectations, escalation routes, runbooks, access, and change responsibilities made explicit.
Quality and reliability in one operating view
Availability, task outcomes, evaluation results, latency, exceptions, incidents, and costs reviewed together.
A controlled improvement cadence
Confirmed failures become prioritized fixes and regression tests, with changes documented and reviewed before release.
Where to apply it
Start with a bounded operating outcome
Each use case is scoped with data access, integration, evaluation, human approval, monitoring, and ownership from the beginning.
Agent operations
Monitor task quality, retrieval, tool use, approvals, escalations, latency, and cost.
Automation reliability
Manage failures, retries, backlogs, connector changes, credentials, and exception queues.
Knowledge operations
Track ingestion, permissions, stale sources, failed synchronization, feedback, and retrieval quality.
AI release management
Version and evaluate prompt, model, policy, tool, and workflow changes before production.
How we deliver
Evidence before scale. Ownership before launch.
The method is intentionally practical: reduce uncertainty early, build the full operating path, and leave the service with people who can run it.
See our delivery approach- 01
Define the operating outcome
Map the work, baseline, users, decisions, exceptions, risks, and evidence required to call the engagement successful.
- 02
Prove the difficult assumptions
Test the data, integrations, model or platform behavior, quality target, and human workflow before scaling the build.
- 03
Build the complete service
Implement identity, data, workflow, evaluation, telemetry, deployment, documentation, and recovery—not only the visible AI feature.
- 04
Release with an owner
Roll out in controlled stages, train the operating team, review production evidence, and convert confirmed failures into improvements.
Typical engagement
What your team receives
The exact scope follows the operating outcome and current environment. These are the core work products typically required to make the result useful and supportable.
Service inventory, ownership, and runbooks
Availability, quality, latency, and cost dashboards
Incident triage and human escalation
Prompt, model, workflow, and connector change control
Scheduled evaluation and drift review
Monthly optimization and governance reporting
Questions to resolve early
Frequently asked questions
What services can you operate?
We support agent, knowledge, document, integration, and automation workloads where access, ownership, support expectations, and platform responsibilities can be clearly defined.
Is this a replacement for our IT team?
No. We define a shared responsibility model. Internal teams retain business and enterprise authority while Capilano AI supplies focused engineering, evaluation, monitoring, incident, and improvement capacity.
What is included in monthly reporting?
Reporting is tailored to the service and may include usage, task outcomes, quality trends, incidents, exceptions, latency, costs, releases, risks, and recommended improvements.
How are production changes approved?
Each material change has an owner, reason, version, test evidence, approval path, deployment record, and rollback plan proportionate to its risk.
Apply it in context
Connect the service to an operating job
Use case
AI quality evaluation and release gates
Turn business tasks, edge cases, and policy requirements into repeatable evaluations, traces, thresholds, and release decisions.
Use case
Managed AI and automation operations
Monitor quality, incidents, cost, integrations, and change across production AI and automation workflows with accountable ownership.
Industry
Contractors & field services
Answer more calls, qualify jobs consistently, coordinate estimates and dispatch, and connect field-service follow-up without replacing the people who own customer and site decisions.
Anonymized delivery pattern
A controlled document-intake pattern for imaging operations
A representative administrative workflow for classifying incoming referral and operational documents, extracting routing metadata, and directing uncertain items to a trained team member.
Field notes
Read the implementation detail
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Bring us the workflow—not a finished AI specification
We will help clarify the operating outcome, difficult assumptions, delivery path, and evidence required for a responsible investment decision.