AI failures are rarely strategic. They are technical. Most AI initiatives that fail do so because of:

- Data that is insufficient, biased, or unavailable at scale.

- Algorithmic approaches that do not generalize beyond controlled settings.

- Performance assumptions that collapse under real-world constraints.

- Lack of defensible moats.

- Systems that cannot scale economically or reliably.

ImpactMode provides independent evaluation of AI initiatives focusing on whether an AI system can work in practice. We asses:

- Core technical feasibility.

- Data realism and constraints.

- Algorithmic limits and known research results.

- Performance ceilings and failure modes.

- System-level and scaling risks.

- Technical defensibility and fragility.

ImpactMode carries out systems-level evaluation grounded in research and practice. Each engagement follows a structured methodology combining:

- Problem framing and assumption validation.

- Data availability and representativeness analysis.

- Algorithmic feasibility checks against current research and benchmarks.

- Performance ceiling estimation and failure-mode identification.

- System-level architecture and scaling risk review.

- Technical defensibility and dependency analysis.

Venture Capital & Investors

- Pre-investment AI technical diligence.

- Early IC filtering or late-stage confirmation.

- Avoiding false positives in AI-first companies.

Enterprises

- Pre-build or pre-scale AI initiative evaluation.

- Independent review of vendor or internal proposals.

- Reducing execution risk before significant spend.