AI feasibility & technical validation
Replace a consequential assumption with evidence
Independent technical investigation for teams deciding whether an AI, software or research direction is feasible—and what evidence would justify the next commitment.
When this is useful
The intended outcome matters, but the architecture, algorithm, data path or research direction is uncertain. The team may have a persuasive demonstration without evidence that the system will work under representative inputs, operating constraints, latency, cost, security or review requirements.
Start with the decision
“Can we build it?” is too broad on its own. A useful investigation names the decision that will change, the assumptions supporting it, the constraints that matter and the evidence that would support proceeding, narrowing or stopping.
Feasibility & Decision Sprint
The standard starting point is a focused assessment of one consequential uncertainty. Work can include research synthesis, architecture analysis, representative data review, algorithm evaluation, independent technical due diligence or an experiment plan. The output is a recommended direction with material risks and remaining unknowns made explicit.
What the decision memo contains
The primary deliverable is a concise technical decision memo designed for use after the engagement. It records the decision under consideration, important constraints and assumptions, evidence examined, viable options, material risks, the recommended direction and the next experiment when further validation is warranted.
Independent Technical Review & Diligence
Assess technical claims, deep-tech feasibility, unusual algorithms, architecture, experimental evidence, research claims and proposed roadmaps for founders, investors or boards. A focused review may fit the Decision Sprint; deeper code, data-room or multi-stakeholder diligence is custom scoped.
Technical Validation Sprint
When the hypothesis, inputs and evaluation criteria are ready, a selected follow-on can test the smallest experiment capable of changing the decision. This may be a model, algorithm experiment, technical spike or limited proof of concept, accompanied by results, limitations and a go, change or stop recommendation.
Scope boundary
A validation sprint focuses on one technical hypothesis. Product design, broad integrations, security or compliance hardening, deployment and ongoing development are separately scoped. The result may support proceeding, changing the approach or ending the investigation.
How this approach appears in prior work
The evidence required for an engagement depends on the decision, system and operating constraints. These project accounts are not templates for a client deliverable; they illustrate how I investigate uncertain questions, test technical claims and report limitations across different domains.
Examples include authorization boundaries in operational AI, industrial data-path integration under real engineering constraints and peer-reviewed evaluation of an explainable-ML method.