Decide what an AI system can see, retrieve, remember, and produce before live data is connected.
Best for
Workflows that connect AI to documents, CRM records, inboxes, or internal knowledge.
Outputs
- -System-of-record map
- -Allowed and restricted context rules
- -Traceability inputs
Separate what AI may recommend, draft, route, or execute from what must remain human-owned.
Best for
Customer, sales, finance, HR, or advisory workflows with business risk.
Outputs
- -Decision-rights boundary
- -Escalation triggers
- -Approval and override model
Prepare AI-supported workflows for outage, drift, degraded context, ownership gaps, and recovery.
Best for
Teams making AI part of recurring operations or business-critical workflows.
Outputs
- -Failure mode map
- -Fallback paths
- -Monitoring and recovery signals
Separate model infrastructure, decision design, implementation scope, governance, and operating cadence.
Best for
Leadership teams deciding where AI should enter operations first.
Outputs
- -Layer map
- -First workflow candidate
- -Architecture brief
Design agentic workflows around triggers, tools, approvals, handoffs, observability, and recovery.
Best for
Teams planning agent workflows beyond a chat interface.
Outputs
- -Tool registry map
- -Approval gates
- -Execution receipts
Decide what knowledge AI should preserve, retrieve, refresh, forget, and cite.
Best for
Teams building internal knowledge, advisory, sales, or operations assistants.
Outputs
- -Memory inventory
- -Retrieval rules
- -Refresh and expiry cadence
Compare workflows by value, context readiness, risk, ownership, recurrence, and implementation effort before choosing what to build.
Best for
Teams turning architecture work into a reusable operating artifact.
Outputs
- -A ranked workflow shortlist
- -A visible risk/value tradeoff
- -A stronger first-system decision
Qualify a use case by decision value, repeatability, context requirements, risk boundaries, and operational ownership.
Best for
Teams turning architecture work into a reusable operating artifact.
Outputs
- -A use-case qualification brief
- -A go/no-go recommendation
- -A clearer implementation scope
Shift the evaluation from model capability alone to the full operating system required for useful AI-supported work.
Best for
Teams turning architecture work into a reusable operating artifact.
Outputs
- -A model-fit rationale
- -Workflow constraints
- -Implementation decision inputs