DecisionShield
An open-source, AI-driven decision readiness and risk audit platform. By analyzing decision context, identifying missing evidence, and computing a decision readiness score, it helps decision-makers assess the completeness of their information rather than offering direct recommendations.
Timeline (last 90 days)
DecisionShield is an AI-driven decision readiness and risk audit platform; it does not immediately pick an option for the user, but first analyzes the decision context, identifies information that supports or weakens the decision, and calculates a 0-100 decision readiness score.
DecisionShield was open-sourced and released by Hacktoberfest Hack Day Coimbatore.
DecisionShield's analysis categories include facts, assumptions, unknowns, claims, risks, missing evidence and stress test scenarios.
DecisionShield uses the Gemma 4 E4B model for semantic reasoning and classification; Gemma generates structured JSON, which after Pydantic validation is handed to a Python decision engine to compute the score.
DecisionShield adopts a separated frontend-backend architecture: the frontend uses React+Vite+Tailwind CSS+Recharts+Framer Motion, the backend uses Python+FastAPI+Pydantic+Uvicorn, the AI layer uses 4-bit quantized local inference of Gemma 4 E4B, and the MVP stage uses in-memory storage.
DecisionShield's stress testing feature draws on risk modeling in finance and fault injection testing in engineering; the user or the system defines hypothetical scenarios, reruns the scoring process one by one, and observes the magnitude of score fluctuations.
DecisionShield's decision readiness score can combine dimensions such as evidence quality, requirement coverage, evidence completeness, risk coverage, assumption load, unknown information and decision stability.
DecisionShield's missing evidence detection identifies, for a database migration scenario, key items to investigate (current query load and performance, transaction and consistency requirements, expected peak traffic) and secondary items (migration downtime tolerance, three-year infrastructure and maintenance costs, the team's experience with the new database).
In DecisionShield's stress test example, a decision initially scoring 72 points may drop to 43 points under a scenario with newly added compliance requirements.
DecisionShield's roadmap includes evidence ingestion from documents/PDFs/spreadsheets, evidence traceability, advanced decision graphs, collaborative decision rooms, decision history, external knowledge verification, more complex stress testing, and specialized audit templates for domains such as software architecture, cloud infrastructure, procurement, hiring and business strategy.
All Facts (10)
DecisionShield is an AI-driven decision readiness and risk audit platform; it does not immediately pick an option for the user, but first analyzes the decision context, identifies information that supports or weakens the decision, and calculates a 0-100 decision readiness score.
50%UnverifiedDecisionShield's missing evidence detection identifies, for a database migration scenario, key items to investigate (current query load and performance, transaction and consistency requirements, expected peak traffic) and secondary items (migration downtime tolerance, three-year infrastructure and maintenance costs, the team's experience with the new database).
50%UnverifiedIn DecisionShield's stress test example, a decision initially scoring 72 points may drop to 43 points under a scenario with newly added compliance requirements.
50%UnverifiedDecisionShield's analysis categories include facts, assumptions, unknowns, claims, risks, missing evidence and stress test scenarios.
50%UnverifiedDecisionShield uses the Gemma 4 E4B model for semantic reasoning and classification; Gemma generates structured JSON, which after Pydantic validation is handed to a Python decision engine to compute the score.
50%UnverifiedDecisionShield adopts a separated frontend-backend architecture: the frontend uses React+Vite+Tailwind CSS+Recharts+Framer Motion, the backend uses Python+FastAPI+Pydantic+Uvicorn, the AI layer uses 4-bit quantized local inference of Gemma 4 E4B, and the MVP stage uses in-memory storage.
50%UnverifiedDecisionShield's stress testing feature draws on risk modeling in finance and fault injection testing in engineering; the user or the system defines hypothetical scenarios, reruns the scoring process one by one, and observes the magnitude of score fluctuations.
50%UnverifiedDecisionShield's decision readiness score can combine dimensions such as evidence quality, requirement coverage, evidence completeness, risk coverage, assumption load, unknown information and decision stability.
50%UnverifiedDecisionShield was open-sourced and released by Hacktoberfest Hack Day Coimbatore.
50%UnverifiedDecisionShield's roadmap includes evidence ingestion from documents/PDFs/spreadsheets, evidence traceability, advanced decision graphs, collaborative decision rooms, decision history, external knowledge verification, more complex stress testing, and specialized audit templates for domains such as software architecture, cloud infrastructure, procurement, hiring and business strategy.
50%