Commercial Prospecting

Purpose

• Research companies and individuals to build qualified prospect profiles for technology consulting services.
• Assess fit against ICP criteria, evaluate tech maturity across Software/Data/AI axes, and produce actionable intelligence for outreach.

Scope

• This skill WILL:

  • Research and compile company intelligence from available sources
  • Assess technology maturity across Software, Data, and AI axes
  • Score ICP fit with dimension-level breakdown
  • Identify key contacts and likely pain points
  • Recommend approach angles (service line, lead pain)
  • Surface competitive landscape signals
  • Update the commercial pipeline state

• This skill WILL NOT:

  • Draft outreach messages or sequences
  • Conduct discovery calls or meeting preparation
  • Negotiate or make pricing decisions
  • Fabricate or assume company data

Inputs

• commercial-state.md — current pipeline state.
• user_input — company name, industry, or target criteria.
• Optional: company website URL, LinkedIn profile, existing intel.

Workflow

1. Company Research

• Gather publicly available information: industry, employee count, revenue range, geography, founding year.
• Identify digital presence signals: website technology, tech job postings, public repositories, engineering blog, conference participation.
• Note recent events: funding rounds, M&A activity, leadership changes, digital transformation announcements.

2. Tech Maturity Assessment

Score the company on three axes using a 1-5 scale:

Axes:

• Software: Development practices, architecture, DevOps, deployment maturity.
• Data: Data infrastructure, governance, analytics capability, self-service BI.
• AI: ML in production, data science team, AI strategy, experimentation infrastructure.

The sweet spot for consulting is companies scoring 2-3. Companies at 1 lack readiness; companies at 5 rarely need external help.

Each score MUST include evidence or justification.

3. ICP Scoring

Score against six dimensions (total 0-100). See references/icp-framework.md for the full scoring rubric.

Show dimension breakdown, not just total.

4. Contact Identification

• Identify key decision makers and influencers (name, role).
• Map likely pain points per contact based on role and company context.

5. Approach Recommendation

• Determine which service line to lead with (Software Engineering, Data Engineering, AI/ML).
• Identify which pain point to open with.
• Note competitive landscape: who else might be pitching this company.
• Do NOT recommend approach without sufficient intel — flag gaps instead.

Outputs

1. New File: prospect-profile.md

# Prospect Profile: {Company Name}

## Company Overview
- **Industry**:
- **Size**: employees / revenue range
- **Geography**:
- **Website**:

## Tech Maturity Assessment
| Axis | Score (1-5) | Justification |
|------|-------------|---------------|
| Software | | |
| Data | | |
| AI | | |

## ICP Fit Score
| Dimension | Max | Score | Justification |
|-----------|-----|-------|---------------|
| Company size & budget capacity | 20 | | |
| Technology maturity gap | 25 | | |
| Industry alignment | 15 | | |
| Decision-making accessibility | 15 | | |
| Growth trajectory | 15 | | |
| Cultural fit for consulting | 10 | | |
| **Total** | **100** | **XX** | |

**Tier**: 1 / 2 / 3 / 4

## Key Contacts
| Name | Role | Likely Pain Points |
|------|------|--------------------|
| | | |

## Recommended Approach
- **Lead service line**:
- **Lead pain point**:
- **Competitive landscape**:

## Data Freshness
- Research date: YYYY-MM-DD
- Sources: [list]
- Gaps: [list unknowns]

2. Updated: commercial-state.md

Add or update entry in the Account Intelligence section with prospect tier, ICP score, and recommended next action.

Guardrails

1. Never fabricate company data — mark unknowns explicitly as [Unknown] or [Estimated].
2. Tech maturity assessment must include evidence/justification for each score.
3. ICP score must show dimension breakdown, not just a total.
4. Do not recommend outreach approach without sufficient intel — flag gaps instead.
5. Flag when prospect data is stale (>90 days from research date).

Example

Input: “Research LogiTrack Solutions for our pipeline. Mid-market logistics company, ~400 employees, based in Bogotá.”

Output (excerpt):

# Prospect Profile: LogiTrack Solutions

## Company Overview
- **Industry**: Logistics & Supply Chain
- **Size**: ~400 employees / estimated USD $30-50M revenue
- **Geography**: Bogotá, Colombia
- **Website**: [Unknown]

## Tech Maturity Assessment
| Axis | Score (1-5) | Justification |
|------|-------------|---------------|
| Software | 2 | Job postings show legacy Java/.NET stack, no CI/CD mentions |
| Data | 2 | Uses Excel-based reporting, no dedicated data team |
| AI | 1 | No AI/ML roles or initiatives identified |

## ICP Fit Score
| Dimension | Max | Score | Justification |
|-----------|-----|-------|---------------|
| Company size & budget capacity | 20 | 16 | 400 employees, mid-market — strong fit |
| Technology maturity gap | 25 | 22 | Scores 2/2/1 — high need, sufficient baseline |
| Industry alignment | 15 | 12 | Logistics has strong consulting demand |
| Decision-making accessibility | 15 | 10 | Mid-market, likely reachable CTO |
| Growth trajectory | 15 | 9 | [Estimated] stable growth in LatAm logistics |
| Cultural fit for consulting | 10 | 7 | Mid-market LatAm companies generally open to consulting |
| **Total** | **100** | **76** | |

**Tier**: 2 (Standard)

## Key Contacts
| Name | Role | Likely Pain Points |
|------|------|--------------------|
| [Unknown] | CTO / VP Engineering | Legacy modernization, scaling challenges |
| [Unknown] | COO | Operational visibility, route optimization |

## Recommended Approach
- **Lead service line**: Data Engineering
- **Lead pain point**: Lack of operational visibility — Excel-based reporting limits decision speed
- **Competitive landscape**: [Unknown] — research needed

## Data Freshness
- Research date: 2026-02-20
- Sources: Job postings, industry reports
- Gaps: Website, revenue confirmation, org chart, competitive landscape