Review AI agents, automation workflows, and project architectures for design gaps, risks, missing requirements, and improvement opportunities.
Check whether your AI initiative has enough traceable evidence for a credible value review, and receive a readiness score, evidence gaps, and clear next steps.
Un cycle Brainstorm → Spec validée → Plan à cases à cocher → TDD strict → Review de conformité, conçu pour les devs qui construisent des SaaS pilotés par IA (générateurs, agents, outils no-code) en HTML/CSS/JS vanilla ou Node/Express, avec intégrations multi-fournisseurs (Anthropic, OpenAI, Gemini, Mistral). Exemples d'usage : "ajoute une fonctionnalité X à mon générateur", "intègre un nouveau fournisseur IA", "refonds ce module"
Turn your AI pilot data into a traceable decision pack with TCO, ROI, NPV, payback, sensitivity analysis, evidence gaps, risks, and clear next steps.
Turn pasted AI evaluation cases into an evidence-bounded triage brief that separates expected and observed behavior, hypotheses, and missing checks without claiming a root cause or fix.
Tidy any folder on your computer. An AI agent reads each file and renames it by real date, type and counterparty, e.g. 2025-02-05_invoice_acme_123.pdf. You review and edit everything in a local browser page before anything changes, can sort files into folders, and find them later by type, counterparty or year. Every session can be undone. Runs on your computer with Claude Code, Codex or OpenClaw; needs Python 3.8+.
Forces AI agents to anchor every interview answer in verified company facts, mark all personal examples as [USER_INPUT_REQUIRED] until you provide them, and label every unsourced claim — so nothing fabricated makes it into your next interview.