Services
Six ways to make AI spend accountable
Each service stands alone. Most clients start with one and add another once the first proves out.
AI opportunity assessment
Most organizations have more AI ideas than capacity to execute them, and no comparable way to choose between them.
What you get
- A prioritized, scored use case registry
- A 12-month sequencing roadmap
- Go and no-go criteria for each use case
Workflow automation
Knowing a process is manual and slow is not the hard part. Knowing where it can tolerate error and where it cannot is.
What you get
- A documented workflow map with risk classification
- A working automation with defined guardrails
- A monitoring specification
Model strategy and cost optimization
Cut inference cost without touching output quality, by routing each task to the cheapest model that clears the bar.
What you get
- A cost breakdown by workflow
- A tested model recommendation per task
- A cascade routing design
Governance, risk and compliance
When legal and risk teams are not given what they need to approve an AI use case, the default answer becomes no.
What you get
- A use case risk classification
- A written acceptable-use policy
- Implemented guardrails
Executive advisory
AI leaders are increasingly expected to report a return figure to the board. Most do not yet have the infrastructure to produce one.
What you get
- A board-ready reporting framework
- A written point of view document
- Structured briefing sessions
Enablement and training
Companies buy AI tool licenses at scale. Adoption stays low, and the return on that spend never materializes.
What you get
- A usage audit against license spend
- Role-specific training curricula
- Measured before and after adoption data
Not sure which service is the right starting point?
An AI ROI assessment tells you which of these to do first.