Founders and CEOs
Build / AI strategy & evaluation
AI strategy, opportunity evaluation, and technical due diligence
Decide where AI is useful, what should be built or bought, what must remain deterministic, and what evidence should govern the investment.
Turn a long list of AI ideas into a small number of defensible decisions, experiments, and stop conditions.
When to call
These are useful signals that the next decision needs more than another tool, vendor demonstration, backlog item, or workshop.
- Every department has an AI idea but no common decision method
- Pilots are accumulating without a production owner or acceptance model
- A vendor demonstration is being treated as proof of task fit
- ROI estimates ignore exception load, review, integration, and operation
- The organization needs an independent technical second opinion
The outcomes
- A use-case inventory ranked by value, feasibility, risk, and learning cost
- A build, buy, integrate, automate, defer, or stop recommendation
- A representative proof plan for the hardest assumptions
- An architecture and provider decision tied to operating constraints
- A portfolio roadmap with owners, gates, dependencies, and evidence
What leaves the engagement
The exact artifact set is scoped to the decision, but the intended result is working behavior, visible evidence, and an owner—not a report that cannot be operated.
- AI opportunity and workflow inventory
- Value, feasibility, data, risk, and readiness scoring
- Vendor, model, and architecture evaluation
- Build/buy/partner and total-cost analysis
- Production Proof Sprint plan
- Executive decision record and phased roadmap
How the work proceeds
- Start from work and value. We examine users, decisions, volume, delay, error, cost, and the consequence of failure before selecting a model or platform.
- Compare the actual alternatives. AI is evaluated against policy change, process redesign, conventional software, deterministic automation, vendor products, and doing nothing.
- Price the whole system. The estimate includes integration, data preparation, evaluation, review, security, monitoring, exceptions, change, and switching—not only model tokens.
- Buy evidence cheaply. The first experiment targets the most decision-relevant uncertainty. Passing it earns the next investment; failing it changes or stops the plan.
Limits that stay explicit
Serious implementation work includes the conditions under which its claims do not hold.
- Strategy is a decision system, not a prediction that eliminates uncertainty.
- Public benchmarks and vendor claims must be tested against representative internal tasks.
- Financial benefits remain hypotheses until measured in the operating workflow.
- Regulatory applicability, investment advice, and legal conclusions require qualified specialists.
Questions teams ask
Can you evaluate an AI vendor or acquisition target?
Yes. We can examine product behavior, architecture, data, evaluations, security, operations, cost, dependencies, roadmap, and evidence within an authorized technical diligence scope.
What happens if the right answer is not to build?
That is a valid outcome. The engagement should prevent waste as well as identify opportunity, so we record the evidence, alternative, and conditions that would justify revisiting the decision.
AI strategy & evaluation / first decision
Bring the real constraint.
Tell us the current state, the outcome that matters, and what has already been tried. The first conversation is for fit and truth—not a promise made before the system is understood.
Please do not send secrets, credentials, regulated data, or confidential customer material through an initial inquiry.