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DECISIONSHIELD · AI-POWERED DECISION READINESS & RISK AUDIT

AI Can Answer.

But Can You Trust the Decision?

Should our startup migrate from PostgreSQL to MongoDB?

Facts · Assumptions · Unknown information

Missing evidence · Risks · Conflicting requirements

The problem isn't getting an answer. It's knowing whether you have enough evidence to act on it.

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THE POSITIONING

We are not building a better ChatGPT

GENERAL AI ASSISTANT

Answers the question.

    • Answers questions
    • Generates recommendations
    • Leaves the user to interpret the gaps

ChatGPT, Claude, and Gemini are general-purpose assistants.

DECISIONSHIELD

Audits the decision.

    • Extracts facts and assumptions
    • Finds missing evidence
    • Scores readiness
    • Stress-tests scenarios
    • Keeps the human in control

A domain-specific decision-audit methodology.

LangChain and LangGraph are frameworks for building AI workflows. DecisionShield applies those concepts to a structured decision-audit platform.

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THE PRODUCT LOOP

DecisionShield turns a question into an audit

01

User decision

02

Understand context

03

Extract evidence

04

Classify

05

Find gaps

06

Stress test

07

Measure

08

Decision audit

DECISION AUDIT DASHBOARD

Metric

Value

Decision readiness

64 / 100

Status

NOT READY

Critical gaps

3

Assumptions

4

Unknowns

5

Risks

3

Decision stability

MEDIUM

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TECHNICAL ARCHITECTURE

Gemma understands the decision. Our engine measures it.

Architecture: User → Decision Context → LangGraph / AI Workflow → Gemma → Structured JSON → Validation Layer → Deterministic Decision Engine → Decision Readiness Score → Audit Report.

Gemma: fact extraction, assumption detection, risk identification, missing-evidence discovery, contradiction detection, and stress-scenario generation.

Deterministic engine: evidence coverage, requirement coverage, risk coverage, assumption penalty, and unknown-information penalty.

Gemma provides reasoning and structured analysis. It is not allowed to directly determine the final numerical score. The deterministic engine provides reproducible scoring.

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SCENARIO STRESS TEST

Before you decide, break the decision

A strong decision should survive reasonable changes in its assumptions.

    • Establish baseline
    • Identify critical variables and assumptions
    • Generate realistic what-if scenarios
    • Modify decision variables
    • Re-evaluate options
    • Measure impact and detect decision flips
    • Identify new risks and calculate stability

Budget decreases by 50%: readiness 72 → 51.

SCENARIO STRESS TEST

A reasonable change in assumptions flips the recommendation from MongoDB to PostgreSQL.

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THE OUTPUT

Not just a recommendation. A decision audit.

DECISION READINESS

64

/ 100

NOT READY

Decision stability: Medium

Key facts

5 verified facts

Critical assumptions

4 assumptions

Missing evidence

3 critical gaps

Top risks

3 major risks

NEXT ACTIONS

    • Verify transaction requirements
    • Benchmark query workload
    • Confirm 3-year infrastructure cost

Instead of telling the user what to choose, DecisionShield tells them what they need to know before choosing.

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Don't ask AI what to choose. Ask AI if you're ready to choose.

CHATBOT

Question → Answer

DECISIONSHIELD

Decision → Evidence → Risk → Stress Test → Readiness → Action

Better decisions: expose hidden assumptions and missing evidence.

Explainable AI: trace every readiness result to measurable factors.

Human-controlled: AI analyzes and stress-tests; humans make the final decision.

DecisionShield — AI-Powered Decision Readiness & Risk Audit

Gemma 4 · LangGraph · FastAPI · React · Deterministic Decision Engine