Commercial Discovery

Inputs

• prospect-profile.md — from commercial-prospecting
• commercial-state.md — pipeline context
• user_input — meeting details, known contacts, specific areas to explore

Outputs (contract)

Output 1: Pre-Meeting Brief (discovery-prep.md)

• Company research summary — key facts, recent news, strategic context
• Known pain points and hypotheses — from prospecting or prior interactions
• Stakeholder map — known contacts, roles, likely agenda
• SPIN question guide — 15-20 questions tailored to this prospect, organized by S/P/I/N (see references/discovery-frameworks.md)
• Meeting agenda suggestion — 45-60 min structure
• Red flags to watch for — signals this opportunity may not be real
• Success criteria for the meeting — what "good" looks like

Output 2: Post-Meeting Discovery Notes (discovery-notes.md)

• Meeting metadata — date, attendees, duration
• Current State summary — tech stack, processes, team, pain points
• Desired Future State — what success looks like for them
• Gap Analysis — current → desired, organized by Software / Data / AI
• Buying Committee Map — Champion, Economic Buyer, Technical Buyer, Coach, Blocker — with names
• Budget signals — explicit mentions, inferred range
• Timeline signals — urgency drivers, deadlines, fiscal year
• Competition signals — other vendors mentioned, internal alternatives
• Next steps agreed
• Open questions requiring follow-up

Output 3: Updated commercial-state.md

Update the opportunity with discovery insights: stage, champion, key pain points, next action.

SPIN Framework Adapted for Tech Consulting

• Situation: Current tech landscape, team structure, processes, recent initiatives
• Problem: Pain points, inefficiencies, failed past initiatives, technical debt
• Implication: Business impact of not solving (revenue loss, competitive risk, team attrition, compliance risk)
• Need-payoff: Value of solving (ROI, speed, capability unlock, market advantage)

For the full SPIN question bank organized by service line (Software, Data, AI) with 10 questions per category, see references/discovery-frameworks.md.

Mini Tech Maturity Assessment (during discovery)

• Run a quick 5-question assessment per axis (Software / Data / AI) to validate or update prospecting scores.
• Compare self-reported maturity vs. observed indicators.
• Full questionnaire available in references/discovery-frameworks.md.

Guardrails (must follow)

1. Discovery is about listening, not pitching — question-to-statement ratio should be 3:1 minimum.
2. Never propose a solution during discovery — note the urge, defer to solution-design phase.
3. Always map at least Champion + Economic Buyer — if you cannot identify both, flag as risk.
4. Capture exact quotes when possible — client's own words are gold for proposals.
5. Never assume budget — probe with indirect questions.
6. If discovery reveals the prospect is not a fit, say so honestly rather than forcing it.
7. Flag when a single discovery meeting is insufficient and recommend follow-up.

Example

Context: Logistics company (Acme Logistics) exploring data platform modernization. Legacy SQL Server data warehouse, 15-person IT team, $40M revenue.

Sample SPIN Questions:

Situation:

• "Walk me through how data currently flows from your TMS and WMS into the SQL Server warehouse."
• "How many people on the team write queries or reports against the warehouse today?"

Problem:

• "What happens when leadership asks for a report that crosses multiple source systems?"
• "How long does it take to onboard a new data source into the warehouse?"

Implication:

• "When route optimization decisions are delayed because data isn't ready, what's the cost per day in fuel and driver hours?"
• "If the warehouse goes down during peak shipping season, what's the operational impact?"

Need-payoff:

• "If your operations team had real-time visibility into shipment status across all carriers, how would that change your customer SLA performance?"
• "What would it mean for the business if you could add a new data source in days instead of months?"