leoyeai/sales-engineer

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