# Business Model Canvas — [Product/Company Name]
_Prepared [date] · Based on [data source: e.g., "5 customer interviews", "market research", "board brief"]_
## Executive Summary
[2–3 sentences: what you're building, for whom, and the core business bet]
## Customer Segments
### Primary Segment: [Segment Name]
- **Who:** [Specific definition: role, company size, geography]
- **Problem:** [What they're trying to solve — customer quote if available]
- **Size:** [TAM estimate if available]
- **Buying power:** [Who decides, what's the approval process]
### Secondary Segment: [Segment Name]
[Same structure]
## Value Propositions
### For [Primary Segment]
- **Core value:** [One sentence: what changes for them]
- **Top 3 benefits:**
1. [Benefit with customer evidence if available]
2. [Benefit]
3. [Benefit]
### For [Secondary Segment]
[Same structure]
## Channels
| Segment | Discovery | Acquisition | Retention | Relationship Type |
|---------|-----------|------------|-----------|------------------|
| [Segment 1] | [Channel] | [Channel] | [Channel] | [Type] |
| [Segment 2] | [Channel] | [Channel] | [Channel] | [Type] |
## Key Resources
- **Technology:** [Core tech, infrastructure, platforms]
- **Team:** [Critical roles and skills needed]
- **Data/IP:** [Data assets, proprietary models, brand]
- **Partnerships:** [Strategic relationships required to launch]
## Key Activities
1. [Critical activity 1] — [Why it matters]
2. [Critical activity 2] — [Why it matters]
3. [Critical activity 3] — [Why it matters]
4. [Critical activity 4] — [Why it matters]
## Revenue Streams
| Segment | Model | Price | Annual Value | Notes |
|---------|-------|-------|--------------|-------|
| [Segment 1] | [Model: SaaS/one-time/% of value] | [$X] | [$X per customer] | [Assumptions] |
| [Segment 2] | [Model] | [$X] | [$X per customer] | [Assumptions] |
**Total addressable market:** [Estimated TAM based on segments]
## Cost Structure
### Fixed Costs (Monthly/Annual)
- [Cost category]: $[X]K — [What it covers]
- [Cost category]: $[X]K — [What it covers]
### Variable Costs (Per Customer/Unit)
- [Cost category]: [Cost model] — [What it covers]
- [Cost category]: [Cost model] — [What it covers]
**Gross margin target:** [X%]
**Unit economics:** LTV:CAC ratio of [X:1] needed for sustainable growth
## Profitability Timeline
- **T-0:** Launch with [funding/revenue]
- **T-12 months:** [Key metrics: revenue, customers, burn rate]
- **T-24 months:** Break-even or profitability target
- **Key dependencies:** [What must happen to hit this timeline]
## Critical Assumptions (Riskiest First)
1. **[Assumption]:** Currently [status]. Validation method: [how you'll test this]
2. **[Assumption]:** Currently [status]. Validation method: [how you'll test this]
3. **[Assumption]:** Currently [status]. Validation method: [how you'll test this]
## Interdependencies & Scenario Analysis
### If Primary Segment Doesn't Exist
[What shifts in the model: alternate segments, channels, revenue, costs]
### If CAC is 3x Higher Than Assumed
[Does the unit economics still work? What changes?]
### If Customer Lifetime is Half What We Expect
[Does the model break? What's the mitigation?]
## Next Steps
1. [Immediate validation needed]
2. [This week]
3. [Before funding decision]
# Business Model Canvas — SearchCode (Enterprise Code Search)
_Prepared 2026-03-15 · Based on 12 discovery calls, market research, competitive analysis_
## Executive Summary
SearchCode is an enterprise code search and navigation tool for mid-market engineering teams (50–500 engineers). It replaces fragile grep-based workflows and brittle internal search tools with AI-powered semantic code search. The core bet: engineering teams will pay per-engineer-seat for a tool that turns code discovery from hours into minutes.
## Customer Segments
### Primary Segment: Mid-Market Engineering Teams (Series B–D)
- **Who:** VP Engineering, Engineering Managers, Senior Engineers at SaaS/FinTech/MarTech companies, 50–500 engineers, existing Slack/GitHub
- **Problem:** "Finding the code we need takes 4+ hours per issue. Our internal search is broken. Grep doesn't scale." — VP Engineering, Modular (discovery call 2/27)
- **Size:** ~2,000 companies in US/EU with 50–500 engineers in tech
- **Buying power:** VP Engineering approves, or Engineering Manager champions + CTO approval for tools >$15K/year
### Secondary Segment: Distributed Engineering Teams (Remote-First)
- **Who:** Tech leads and architects at remote-first startups, distributed teams valuing async communication
- **Problem:** "When our team is spread across 3 time zones, pair programming to find code is impossible. We need async code discovery." — CTO, Relay
- **Size:** ~1,500 companies (fast-growing, higher growth rate)
## Value Propositions
### For Mid-Market Teams
- **Core value:** Turn code discovery from hours to minutes with AI semantic search instead of grep
- **Top 3 benefits:**
1. **60% faster debugging** — "If I could search semantically, I'd save 20 hours a month." — Sr. Engineer, Modular (discovery call 2/27)
2. **Onboard engineers 2 weeks faster** — New engineers can find relevant code without asking 5 people
3. **Keep technical knowledge in the codebase** — Reduces bus factor; answers live in code, not Slack
### For Distributed Teams
- **Core value:** Enable async code discovery so engineers don't block on "pair programming" to find code
- **Top 3 benefits:**
1. **Reduce Slack/Zoom overhead** — No more "let me hop on a call to show you where that is"
2. **Better incident response** — Distributed on-call can handle incidents without waking the team
3. **Ship faster across time zones** — No waiting for the person who knows the codebase
## Channels
| Segment | Discovery | Acquisition | Retention | Relationship Type |
|---------|-----------|------------|-----------|------------------|
| Mid-Market Teams | Dev community, HackerNews, engineering blogs, GitHub | Free tier (up to 5 engineers) → paid tier | In-app onboarding, Slack bot, monthly digest | Self-serve + light sales (AE for 100+ engineer deals) |
| Distributed Teams | Dev communities, remote-work forums, product blogs | Free tier + direct outreach | In-app help, async support, community Slack | Self-serve + email support |
## Key Resources
- **Technology:** Semantic code search engine (vector embeddings + HNSW index), IDE plugins (VS Code, JetBrains), GitHub/GitLab API integrations, LLM-based summarization
- **Team:** ML engineer (search ranking), 2 full-stack engineers (product + integrations), 1 DevRel, 1 product
- **Data:** Indexed codebase data (searchable via embedding space), anonymized usage patterns (what engineers search for)
- **Partnerships:** GitHub, GitLab, VS Code Marketplace, Slack, DataDog (for observability)
## Key Activities
1. **Semantic indexing** — Index every codebase update in real-time so search results are always fresh
2. **ML ranking** — Tune ranking model to surface the most relevant code (not just text matches)
3. **Integration maintenance** — Keep IDE plugins, GitHub sync, and API integrations working
4. **Community building** — Maintain developer community, collect feedback, iterate on search quality
## Revenue Streams
| Segment | Model | Price | Annual Value | Notes |
|---------|-------|-------|--------------|-------|
| Mid-Market (Self-serve) | Per-engineer-seat/month | $15/engineer/month | $9,000–$90,000/year (50–500 eng) | Free tier up to 5; convert to paid |
| Mid-Market (Sales) | Annual contract per-seat | $12/seat/month (annual, discounted) | $7,200–$72,000/year | For 100+ engineer deployments |
| Distributed Teams | Per-seat (lower price point) | $8/engineer/month | $4,800–$48,000/year | Price-sensitive segment, higher growth |
**Total addressable market:**
- 2,000 mid-market companies × $30K average = $60M TAM (conservative)
- Growing by 15% YoY as engineering teams expand and code complexity increases
## Cost Structure
### Fixed Costs (Monthly)
- **Infrastructure (compute + storage for vector search):** $40K — Hosting, GPU for indexing, database
- **Team salaries (6 headcount):** $150K — Engineers, product, DevRel
- **Support + tooling:** $8K — Customer support, monitoring, CI/CD
**Total fixed: $198K/month**
### Variable Costs (Per Customer)
- **Indexing** — $50 per customer per month (grows with codebase size)
- **API calls** — $10 per 1,000 search queries
- **Support** — $200 per customer per year (email + community support)
**Gross margin target:** 70% (at scale, fixed costs become leverage)
## Profitability Timeline
- **Month 1:** Launch with $500K seed funding. 50 free tier signups, 0 paid.
- **Month 6:** 300 free-tier users, 20 paid customers (avg $2K MRR). Burn rate $200K/month.
- **Month 12:** 1,500 free users, 80 paid customers ($15K MRR). Burn rate $180K/month.
- **Month 18:** Break-even approaching (80 customers at $18K MRR vs. $180K burn). Pause hiring, focus on sales efficiency.
- **Month 24:** Profitability. $50K MRR from 300 customers. Reinvest in product.
## Critical Assumptions (Riskiest First)
1. **Mid-market teams will pay $12–15/seat for code search:** Currently untested at scale. Validation method: Conduct pricing interviews with 10 current customers in free tier; track conversion rate from free to paid at different price points.
2. **Semantic search + ML ranking significantly outperforms grep:** Partially validated (3/12 discovery calls showed strong preference for semantic). Validation method: A/B test ranking improvements; measure time-to-answer in user studies.
3. **IDE plugins adoption is frictionless:** Not yet tested. Validation method: Track VS Code plugin install rate once launched; conduct usability testing with 5 engineers.
4. **Free tier with 5-engineer limit drives paid conversion:** Not yet tested. Validation method: Monitor free tier usage; set target conversion rate of 5% in first 6 months.
## Interdependencies & Scenario Analysis
### If Target Segment Is Smaller (1,000 companies instead of 2,000)
- **Impact:** TAM shrinks to $30M. Need to win higher % of market to reach scale.
- **Mitigation:** Expand to secondary segments (distributed teams, large enterprises buying "unlimited" contracts); consider vertical focus (FinTech, MarTech).
### If CAC Is 3x Higher ($30K instead of $10K per customer)
- **Impact:** LTV:CAC ratio drops from 3:1 to 1:1. Unit economics break at current pricing.
- **Mitigation:** Move from sales-driven (AE) to product-led (free tier). Invest in community/content to lower CAC below $5K.
### If Semantic Search Advantage Erodes (Competitors Match Quickly)
- **Impact:** Differentiation vanishes. Pricing power drops. Becomes commoditized.
- **Mitigation:** Build moat through ecosystem (integrations, API, plugins). Deepen ML/ranking beyond search (code recommendations, refactoring).
## Next Steps
1. **This week:** Interview 5 current free-tier users on pricing ($12/seat vs. $8/seat willingness-to-pay). Lock on pilot price point.
2. **Next 2 weeks:** Launch free tier to GitHub (ProductHunt, HackerNews). Target 500 signups. Measure time-to-first-search.
3. **Week 4:** Onboard first 5 paid customers (pilot at discounted rate). Collect NPS and feature feedback.
4. **Before Series A:** Hit $10K MRR with 20+ customers. Reduce burn rate to $150K/month.
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_Want me to create a task for pricing interviews, or draft the free tier onboarding flow?_