AI Strategy & Implementation
Find the AI use cases that matter to your economics, then build the workflows, decision systems and data infrastructure to run them.
“Where would AI change our unit economics, decision speed or ability to scale — and what would it take to put it into production?”
What you get
- AI opportunity audit: where the hours, errors and decisions actually sit, ranked by economic weight and feasibility
- Implementation strategy with sequencing, data prerequisites, ownership and the human judgment that stays in the loop
- Agentic workflows, automation and decision-support systems built and handed over with documentation
- Data infrastructure: ingestion pipelines, structured databases and the integration layer the systems depend on
When it applies
- An operations team still runs core analysis by hand in spreadsheets and cannot scale headcount to match demand
- Leadership has approved an AI budget but no one can name the first three use cases worth funding
- A pilot worked in a demo and stalled before production because the data was never in one place