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ATLAS | Agent PWIN Analysis

AI Agent for Probability of Win (PWIN) Modeling

Written by Customer Success Department

Purpose of the AI Agent

This AI Agent quantifies Probability of Win using a structured, 100-point model and provides targeted recommendations to increase PWIN before proposal submission.

User Inputs

  • Opportunity Context: Solicitation details, customer intel, evaluation criteria.

  • Competitive Inputs: Known/likely competitors, teaming considerations, incumbent status.

  • Internal Readiness: Capability fit, past performance relevance, pricing posture, relationships.

What the AI Agent Does

  1. Data Collection and Normalization:

    • Consolidates inputs from qualification analysis, pipeline, and internal assets.

  2. Scoring Across Five Categories (100 points total):

    • Technical Fit, Competitive Position, Business Value, Win Probability, and Risk Assessment.

  3. PWIN Calculation:

    • Computes multiplicative or weighted composite PWIN and confidence range.

  4. Sensitivity and Improvement Plan:

    • Models “what-if” scenarios; identifies actions to raise PWIN with estimated impact.

  5. Reporting and Guidance:

    • Visual indicators, score breakdowns, and prioritized recommendations.

Expected Output

  • File Format: HTML document (PWIN dashboard with scores, rationale, and action plan)

Key Outcome

A defensible, quantified go/no-go assessment with a practical plan to raise win probability.

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