01
Map the decision
We define the business outcome, current baseline, users, systems involved, and the cost of a wrong answer or failed action.
Service
AI Consulting
We come in, look at the actual code and data, and tell you what's worth building, what to buy, and what to walk away from. No 80-page slide decks.
What we build
- Opportunity audits
- Build vs buy analysis
- Architecture review
- Vendor & model selection
- Team coaching
Outcomes we hold ourselves to
- A roadmap with cost & risk attached to each bet
- Killed projects that should have been killed
- Internal teams that can ship without us
Decision guide
What an AI consulting engagement produces
A useful AI strategy is a sequence of testable decisions, not a list of model names. We inspect the workflows, data access, failure costs, security constraints, and existing software behind each opportunity. The result is a build plan your engineering and business teams can challenge before budget is committed.
Best fit for
- Teams with several AI ideas but no defensible way to rank them
- Leaders deciding whether to buy a platform, extend an existing product, or build internally
- Engineering teams that need an architecture, evaluation plan, and realistic operating-cost model
What happens during the engagement
02
Test the risky assumptions
We sample the real data, review integrations, compare model and vendor options, and design a small evaluation that can disprove the idea quickly.
03
Deliver an executable roadmap
You receive a prioritized opportunity register, build-versus-buy recommendation, target architecture, delivery stages, success metrics, and explicit stop conditions.
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