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IT agencies and Startups with 3-20 employees use 3-5 AI tools (ChatGPT, Claude, Gemini, etc.) for work, but without criteria for which tasks to assign to which AI, they waste 3-5 hours per week on trial and error. Inconsistent AI output quality means humans end up reviewing everything, cutting the productivity gains in half.
Recommends the optimal AI model for each task type (code review, document writing, data analysis, translation, etc.) and automatically measures quality scores for task outputs, displayed on a team dashboard. Standardizes repetitive tasks with prompt templates and provides weekly comparison reports on cost, quality, and speed by AI model.
| N Novelty | 1-5 | How uncommon the service is in market context. |
| U Urgency | 1-5 | How urgently users need this problem solved now. |
| M Market | 1-5 | Market size and growth potential from proxy indicators. |
| R Realizability | 1-5 | Buildability for a small team with realistic constraints. |
| V Validation | 1-5 | Validation signal quality from competition and demand data. |
| Tech Complexity | / 40 | Difficulty of core implementation stack. |
| Data Availability | / 25 | Practical availability and cost of required data. |
| MVP Timeline | / 20 | Expected time to ship a usable MVP. |
| API Bonus | / 15 | Bonus for viable public API leverage. |
| Competition | / 20 | Signal quality from competitor landscape. |
| Market Demand | / 20 | Demand proxies from search and mention patterns. |
| Timing | / 20 | Fit with current shifts in tech, behavior, and regulation. |
| Revenue Signals | / 15 | Reference evidence for monetization viability. |
| Pick-Axe Fit | / 15 | How well the concept serves participants in a trend. |
| Solo Buildability | / 10 | Practicality for lean-team implementation. |