sales-engineer

name sales-engineer description Analyzes RFP/RFI responses for coverage gaps, builds competitive feature comparison matrices, and plans proof-of-concept (POC) engagements for pre-sales engineering. Use when responding to RFPs, bids, or proposal requests; comparing product features against competitors; planning or scoring a customer POC or sales demo; preparing a technical proposal; or performing win/loss competitor analysis. Handles tasks described as 'RFP response', 'bid response', 'proposal response', 'competitor comparison', 'feature matrix', 'POC planning', 'sales demo prep', or 'pre-sales engineering'. Sales Engineer Skill 5-Phase Workflow Phase 1: Discovery & Research Objective: Understand customer requirements, technical environment, and business drivers. Checklist: Conduct technical discovery calls with stakeholders Map customer's current architecture and pain points Identify integration requirements and constraints Document security and compliance requirements Assess competitive landscape for this opportunity Tools: Run rfp_response_analyzer.py to score initial requirement alignment. python scripts/rfp_response_analyzer.py assets/sample_rfp_data.json --format json > phase1_rfp_results.json Output: Technical discovery document, requirement map, initial coverage assessment. Validation checkpoint: Coverage score must be >50% and must-have gaps ≤3 before proceeding to Phase 2. Check with: python scripts/rfp_response_analyzer.py assets/sample_rfp_data.json --format json | python -c "import sys,json; r=json.load(sys.stdin); print('PROCEED' if r['coverage_score']>50 and r['must_have_gaps']<=3 else 'REVIEW')" Phase 2: Solution Design Objective: Design a solution architecture that addresses customer requirements. Checklist: Map product capabilities to customer requirements Design integration architecture Identify customization needs and development effort Build competitive differentiation strategy Create solution architecture diagrams Tools: Run competitive_matrix_builder.py using Phase 1 data to identify differentiators and vulnerabilities. python scripts/competitive_matrix_builder.py competitive_data.json --format json > phase2_competitive.json python -c "import json; d=json.load(open('phase2_competitive.json')); print('Differentiators:', d['differentiators']); print('Vulnerabilities:', d['vulnerabilities'])" Output: Solution architecture, competitive positioning, technical differentiation strategy. Validation checkpoint: Confirm at least one strong differentiator exists per customer priority before proceeding to Phase 3. If no differentiators found, escalate to Product Team (see Integration Points). Phase 3: Demo Preparation & Delivery Objective: Deliver compelling technical demonstrations tailored to stakeholder priorities. Checklist: Build demo environment matching customer's use case Create demo script with talking points per stakeholder role Prepare objection handling responses Rehearse failure scenarios and recovery paths Collect feedback and adjust approach Templates: Use assets/demo_script_template.md for structured demo preparation. Output: Customized demo, stakeholder-specific talking points, feedback capture. Validation checkpoint: Demo script must cover every must-have requirement flagged in phase1_rfp_results.json before delivery. Cross-reference with: python -c "import json; rfp=json.load(open('phase1_rfp_results.json')); [print('UNCOVERED:', r) for r in rfp['must_have_requirements'] if r['coverage']=='Gap']" Phase 4: POC & Evaluation Objective: Execute a structured proof-of-concept that validates the solution. Checklist: Define POC scope, success criteria, and timeline Allocate resources and set up environment Execute phased testing (core, advanced, edge cases) Track progress against success criteria Generate evaluation scorecard Tools: Run poc_planner.py to generate the complete POC plan. python scripts/poc_planner.py poc_data.json --format json > phase4_poc_plan.json python -c "import json; p=json.load(open('phase4_poc_plan.json')); print('Go/No-Go:', p['recommendation'])" Templates: Use assets/poc_scorecard_template.md for evaluation tracking. Output: POC plan, evaluation scorecard, go/no-go recommendation. Validation checkpoint: POC conversion requires scorecard score >60% across all evaluation dimensions (functionality, performance, integration, usability, support). If score <60%, document gaps and loop back to Phase 2 for solution redesign. Phase 5: Proposal & Closing Objective: Deliver a technical proposal that supports the commercial close. Checklist: Compile POC results and success metrics Create technical proposal with implementation plan Address outstanding objections with evidence Support pricing and packaging discussions Conduct win/loss analysis post-decision Templates: Use assets/technical_proposal_template.md for the proposal document. Output: Technical proposal, implementation timeline, risk mitigation plan. Python Automation Tools 1. RFP Response Analyzer Script: scripts/rfp_response_analyzer.py Purpose: Parse RFP/RFI requirements, score coverage, identify gaps, and generate bid/no-bid recommendations. Coverage Categories: Full (100%), Partial (50%), Planned (25%), Gap (0%). Priority Weighting: Must-Have 3×, Should-Have 2×, Nice-to-Have 1×. Bid/No-Bid Logic: Bid: Coverage >70% AND must-have gaps ≤3 Conditional Bid: Coverage 50–70% OR must-have gaps 2–3 No-Bid: Coverage <50% OR must-have gaps >3 Usage: python scripts/rfp_response_analyzer.py assets/sample_rfp_data.json # human-readable python scripts/rfp_response_analyzer.py assets/sample_rfp_data.json --format json # JSON output python scripts/rfp_response_analyzer.py -- help Input Format: See assets/sample_rfp_data.json for the complete schema. 2. Competitive Matrix Builder Script: scripts/competitive_matrix_builder.py Purpose: Generate feature comparison matrices, calculate competitive scores, identify differentiators and vulnerabilities. Feature Scoring: Full (3), Partial (2), Limited (1), None (0). Usage: python scripts/competitive_matrix_builder.py competitive_data.json # human-readable python scripts/competitive_matrix_builder.py competitive_data.json --format json # JSON output Output Includes: Feature comparison matrix, weighted competitive scores, differentiators, vulnerabilities, and win themes. 3. POC Planner Script: scripts/poc_planner.py Purpose: Generate structured POC plans with timeline, resource allocation, success criteria, and evaluation scorecards. Default Phase Breakdown: Week 1: Setup — environment provisioning, data migration, configuration Weeks 2–3: Core Testing — primary use cases, integration testing Week 4: Advanced Testing — edge cases, performance, security Week 5: Evaluation — scorecard completion, stakeholder review, go/no-go Usage: python scripts/poc_planner.py poc_data.json # human-readable python scripts/poc_planner.py poc_data.json --format json # JSON output Output Includes: Phased POC plan, resource allocation, success criteria, evaluation scorecard, risk register, and go/no-go recommendation framework. Reference Knowledge Bases Reference Description references/rfp-response-guide.md RFP/RFI response best practices, compliance matrix, bid/no-bid framework references/competitive-positioning-framework.md Competitive analysis methodology, battlecard creation, objection handling references/poc-best-practices.md POC planning methodology, success criteria, evaluation frameworks Asset Templates Template Purpose assets/technical_proposal_template.md Technical proposal with executive summary, solution architecture, implementation plan assets/demo_script_template.md Demo script with agenda, talking points, objection handling assets/poc_scorecard_template.md POC evaluation scorecard with weighted scoring assets/sample_rfp_data.json Sample RFP data for testing the analyzer assets/expected_output.json Expected output from rfp_response_analyzer.py Integration Points Marketing Skills - Leverage competitive intelligence and messaging frameworks from marketing-skill/ Product Team - Coordinate on roadmap items flagged as "Planned" in RFP analysis from product-team/ C-Level Advisory - Escalate strategic deals requiring executive engagement from c-level-advisor/ Customer Success - Hand off POC results and success criteria to CSM from ../customer-success-manager/ Last Updated: February 2026 Status: Production-ready Tools: 3 Python automation scripts References: 3 knowledge base documents Templates: 5 asset files