Most teams treat AI as a series of one-off projects. We run it as a managed service: monitored workflows, governance, reporting, and continuous improvement — so AI stays reliable, controlled, and accountable long after launch.
A Managed Intelligence Provider does for AI what an MSP does for IT — except we were built AI-first. We own the operating model: what's approved, where data goes, what gets monitored, and who is accountable for outcomes.
Every production agent and automation is observed for quality, cost, and reliability.
Approved use cases, data boundaries, and review paths are documented and enforced.
Leadership gets outcomes, usage, and risk in language the board understands.
Assess → architect → deploy → manage, continuously.
Map workflows, shadow AI, data readiness, and the operating case.
Design approved workflows, governance, data boundaries, and review paths.
Build, validate quality, and move only stable workflows to production.
Monitor usage, cost, drift, and outcomes with monthly reviews.
Monitoring and versioning catch drift before users feel it.
Policy and access controls don't decay as adoption grows.
Monthly optimization compounds value instead of letting it stall.
Start with a readiness assessment and we'll design the managed operating model around your workflows, data, and goals.
Infonaligy