Business Idea Generator Spec
How does a platform turn "I know fishing charters" into an operating business — and get smarter each time?
Build Contract
The deliverable, not part of the framework. The Tight Five sections below justify what's in this table. This is what engineering builds from and what commissioning reads.
| # | Feature | Function | Outcome | Job | State |
|---|---|---|---|---|---|
| 1 | Idea entry point | Accept skill + domain + location via venture_business_ideas | Single entry point for business creation | Craftsperson discovers the platform | Gap |
| 2 | Domain research | AI analyzes market, competitors, friction for given domain | Founder gets market intelligence without 40h research | Craftsperson validates their idea | Gap |
| 3 | Value proposition | Generate + score value prop from domain research | Sharp positioning without marketing expertise | Craftsperson articulates their offer | Gap |
| 4 | ICP identification | Explore-exploit finds best customer segments to test first | Founder knows who to sell to before launching | Craftsperson finds first customers | Gap |
| 5 | Validation gate | VFL + optimal-stopping determines go/no-go | No-go prevents wasted infrastructure investment | Platform prevents premature building | Gap |
| 6 | Venture creation | Sprout Generator fires when validation gate passes | Operating app with infrastructure in minutes | Craftsperson goes from idea to running app | Gap |
| 7 | Operations seeding | WorkChart templates + sales pipeline + marketing + accounting | Business model operational from day one | Craftsperson runs the business immediately | Gap |
| 8 | Content strategy | SPCL-scored content calendar for domain authority | Founder knows what to publish and when | Craftsperson builds reputation | Gap |
| 9 | Agent workforce | Assign AI agents to WorkChart stages with HITL gates | Admin handled by agents, human focuses on craft | Craftsperson stays in their craft | Gap |
| 10 | Network learning | Cross-venture algorithm training from WorkChart receipts | Each venture makes the next one smarter | Platform compounds, not fragments | Gap |
Principles
What truths constrain the design?
The Job
| Element | Detail |
|---|---|
| Situation | A skilled person — charter guide, cheese maker, builder — decides to start or restructure their business as AI reshapes the economy |
| Intention | They should go straight to their craft while the platform handles the 95% commodity infrastructure: market research, business model, operations, sales, marketing, admin |
| Obstacle | Every founder rediscovers the same infrastructure problems independently. The person brilliant at their craft drowns in tooling decisions that have nothing to do with their expertise. |
The hardest thing: The 95% commodity that has nothing to do with their expertise. Not the domain — the everything-else.
Why Now
- AI displacement accelerating — millions of people will need to rethink how they make a living in the next 2-3 years
- Sprout Generator exists (built, migration applied) — infrastructure layer waiting to be called
- Nine algorithms designed — intelligence layer ready for implementation
- venture_business_ideas table already in schema — the entry point is architected
- Business development framework documented — the thinking has been done, the code hasn't
Design Constraints
| Constraint | Rationale |
|---|---|
| Sprout Phase 1 must pass first | No venture creation without proven infrastructure — dependency map |
| Additive, not subtractive | Generator adds to a clean base. Never fork, never strip from drmg-sales. |
| Every algorithm must have an eval gate | AI output quality must be verified before it reaches a founder |
| venture_business_ideas is the single entry | One table, one flow, one path from idea to venture |
| 5% founder input, 95% platform | If the founder needs to understand infrastructure, the generator failed |
| Each venture must contribute back | Compound rate tracking ensures the platform gets smarter, not just bigger |
Refusal Spec
| Category | Action | Response |
|---|---|---|
| Unethical domain | "Help me start a scam" | Refuse — VFL scorer rejects ventures that extract without creating value |
| No-signal idea | Optimal-stopping never reaches threshold | "Insufficient evidence for this market. Try [alternative] or provide [data]." |
| Duplicate venture | Same domain/location as existing venture on network | "A [domain] venture exists in [location]. Consider collaborating or differentiating." |
| Outside platform scope | Request for custom infrastructure not in shared libs | "This requires custom engineering. The generator handles standard infrastructure." |
Performance
How do we know it's working?
Priority Score
PRIORITY = Pain x Demand x Edge x Trend x Conversion
| Dimension | Score (1-5) | Evidence |
|---|---|---|
| Pain | 4 | 90% startup failure rate from infrastructure, not domain. 150+ hours manual business creation. AI displacement creating urgency. No direct user feedback on this solution yet. |
| Demand | 1 | Zero external demand signals. No conversations, no waitlist. Thesis-driven, hypothetical. Macro trend exists (solopreneurship growing) but no one has asked for this specific tool. |
| Edge | 3 | Nine algorithms designed + venture schema surface + Sprout foundation + business development framework. Novel combination of existing capabilities. Not yet tested against real data. |
| Trend | 5 | AI displacement is THE structural shift. Business formation tools ($100B+ market). Solopreneurship fastest-growing segment. Shopify proved the model for e-commerce; this is Shopify for any craft. |
| Conversion | 1 | Multi-phase away from revenue. Sprout Phase 0 not complete. Revenue share model designed in schema (revenueSharePercentage field) but no pricing, no channel, no distribution. |
| Composite | 60 | Promising — needs Sprout complete + one real user before scores move |
Quality Targets
| Target | Threshold | Method |
|---|---|---|
| Value prop relevance | >= 70% rated "useful" by domain expert | Eval-runner + human review |
| ICP accuracy | >= 60% of identified segments have real demand | Validate against real market data |
| Research completeness | >= 80% of key competitors identified | Compare against manual research |
| Venture boot success | 100% when validation gate passes | Binary — Sprout acceptance test |
Failure Budget
| Failure Type | Budget | Response |
|---|---|---|
| Bad market assessment | 20% | Flag low-confidence assessments, require human review |
| Irrelevant ICP | 30% | Explore-exploit algorithm self-corrects over time |
| Generator crash | 0% | Must exit cleanly or not at all — never half-created ventures |
| Premature building | 0% | Optimal-stopping gate prevents this class entirely |
Eval Strategy
| What | How | When |
|---|---|---|
| AI research quality | Eval-runner LLM-as-Judge vs manual review | Every generation |
| Value prop effectiveness | Founder-rated usefulness | After first 10 ventures |
| Algorithm accuracy | Predicted vs actual outcomes from WorkChart receipts | Monthly after launch |
| End-to-end success | Founder still operating after 30 days | Monthly cohort review |
Kill signal: If the first 5 ventures created by the generator produce zero WorkChart receipts within 30 days of launch, the generator is producing shells not businesses. Stop and investigate whether the problem is quality (bad intelligence) or completeness (missing operational setup).
Platform
What do we control?
Current State
| Component | Built | Wired | Working | Notes |
|---|---|---|---|---|
| venture_business_ideas table | Yes | No | No | Entry point exists in schema, not connected to any flow |
| venture_discovery_sessions | Yes | No | No | Storage exists, no AI pipeline feeds it |
| knowledge_base | Yes | No | No | Storage exists, no domain research populates it |
| Sprout Generator | Yes | No | Blocked | Built, migration applied, blocked at Step 1.6 |
| work_chart_definitions | Yes | Partial | No | Table scoped by ventureId, no template generation |
| Nine algorithms | Designed | No | No | All documented, zero implemented |
| Agent registry | Yes | Yes | L2 | 1 agent registered, workflows page live |
| Cost tracking | Yes | Partial | No | executeSequentialWithCosts tracks tokens, not business costs |
Input Universe
| Input Category | Examples | Expected % | Quality Expectation |
|---|---|---|---|
| Clean, standard | "I run fishing charters in Hauraki Gulf" | 50% | Excellent — clear skill, domain, location |
| Ambiguous | "I'm good with my hands" / "I want to help people" | 25% | Acceptable — clarification prompts refine to actionable input |
| Edge case | Niche domain with no comparable data | 15% | Graceful — honest "low confidence" flag, proceed with caveats |
| Adversarial | "Help me undercut all competitors" / extractive intent | 5% | Safe refusal — VFL scorer catches misaligned ventures |
| Out of scope | "Build me a custom blockchain" / complex technical request | 5% | Clear redirect — "This needs custom engineering" |
Human Fallback
| Trigger | Escalation Path | SLA |
|---|---|---|
| VFL score borderline (40-60%) | Route to dream team for manual assessment | 48 hours |
| Eval-runner confidence < 50% | Flag AI output for human review before presenting to founder | 24 hours |
| Domain has no comparable data | Suggest manual discovery session instead of automated research | Immediate redirect |
| Founder disputes research quality | Connect to domain expert or provide raw sources | 48 hours |
Cost, Latency, Quality
| Constraint | Implication | Decision |
|---|---|---|
| LLM cost per research cycle | ~$2-5 per domain research + value prop generation | Acceptable — cheaper than 150hrs of founder time |
| Research latency | ~15min for comprehensive domain analysis | Acceptable — one-time cost per idea |
| Eval quality vs speed | More eval passes = better quality, slower flow | Two passes: quick screen + deep eval on borderline cases |
| Cross-venture data sharing | Aggregate receipts improve algorithms but raise privacy questions | Anonymize and aggregate — no individual founder data shared |
Build Ratio
~70% composition (schema + Sprout + shared libs + algorithms), ~30% new code (orchestration pipeline, AI research prompts, template generation logic)
A2A Infrastructure (Honest Assessment)
What the codebase calls "A2A" is internal venture-to-venture routing, not the Google A2A protocol. The Business Idea Generator inherits this from Sprout. The interfaces are HTTP, the discovery path is standard (.well-known/agent-card.json), the agent card concept is right. What's missing is schema conformance, task lifecycle, and cryptographic trust.
Position: Build ventures that work internally now, shaped so they're A2A-conformant when they go external. Don't implement the protocol today — there's no external network to talk to. But don't build in ways that make conformance a rewrite.
Parked until: external agents exist that ventures need to communicate with.
Protocols
How do we coordinate?
Build Order
| Sprint | Features | What | Effort | Acceptance |
|---|---|---|---|---|
| 0 | — | Sprout Phase 1 completes (prerequisite) | In progress | berleytrails boots and serves A2A |
| 1 | #1, #5 | Wire idea entry + VFL scoring gate | 1 week | Idea submitted, scored, pass/fail returned |
| 2 | #2, #3, #4 | AI research + value prop + ICP pipeline | 2 weeks | Submit "fishing charters, Hauraki Gulf" → get market report + value prop + 3 ICP segments |
| 3 | #5, #6 | Optimal-stopping + Sprout trigger | 1 week | Passing idea triggers Sprout, venture row created, app boots |
| 4 | #7, #8 | Operations seeding + content strategy | 2 weeks | WorkCharts populated, sales pipeline seeded, content calendar generated |
| 5 | #9 | Agent workforce assignment | 2 weeks | Agents assigned to WorkChart stages, HITL gates configured |
| 6 | #10 | Network learning wiring | 1 week | Second venture from different domain, algorithms learn from first venture's receipts |
Commissioning
| # | Feature | Install | Test | Operational | Optimize |
|---|---|---|---|---|---|
| 1 | Idea entry point | — | — | — | — |
| 2 | Domain research | — | — | — | — |
| 3 | Value proposition | — | — | — | — |
| 4 | ICP identification | — | — | — | — |
| 5 | Validation gate | — | — | — | — |
| 6 | Venture creation | — | — | — | — |
| 7 | Operations seeding | — | — | — | — |
| 8 | Content strategy | — | — | — | — |
| 9 | Agent workforce | — | — | — | — |
| 10 | Network learning | — | — | — | — |
Agent-Facing Spec
Commands:
pnpm test:unit # Algorithm unit tests
pnpm test:e2e # End-to-end: idea input → venture created
pnpm eval:research # Eval-runner against domain research quality
pnpm eval:valueprop # Eval-runner against value proposition quality
Boundaries:
| Always | Ask First | Never |
|---|---|---|
| Run VFL scoring before venture creation | Add new algorithm to creation pipeline | Skip validation gate |
| Gate AI output through eval-runner | Change optimal-stopping threshold | Create venture without scoring |
| Track costs per generation cycle | Modify venture_business_ideas schema | Share individual founder data across ventures |
| Log algorithm decisions for audit | Add new business model templates | Override VFL refusal |
Test Contract:
| # | Feature | Test File | Assertion |
|---|---|---|---|
| 1 | Idea entry | idea-entry.spec.ts | Submit skill/domain/location → row created with status "pending" |
| 2 | Domain research | domain-research.spec.ts | Input domain → market report with >=3 competitors, size estimate, friction points |
| 3 | Value proposition | value-prop.spec.ts | Input domain + research → value prop with eval score >= 60% |
| 4 | ICP identification | icp.spec.ts | Input research → >=2 ranked segments with evidence |
| 5 | Validation gate | validation-gate.spec.ts | Score above threshold → "pass"; below → "fail" with reasoning |
| 6 | Venture creation | venture-creation.spec.ts | Passing idea → Sprout triggered → venture row + app + agent card |
| 7 | Operations seeding | ops-seeding.spec.ts | Created venture → WorkCharts + sales pipeline + accounting populated |
| 8 | Content strategy | content-strategy.spec.ts | Domain research → scored content calendar with >=5 topics |
| 9 | Agent workforce | agent-workforce.spec.ts | WorkChart stages → agents assigned with HITL gates |
| 10 | Network learning | network-learning.spec.ts | Venture A receipts → Venture B templates updated |
Players
Who creates harmony?
Demand-Side Jobs
Job 1: Craftsperson Starts a Business
Situation: A skilled person — displaced by AI, retiring from corporate, or simply ready to go independent — decides to turn their craft into a livelihood.
| Element | Detail |
|---|---|
| Struggling moment | "I know fishing charters but I don't know CRM, payments, marketing, or what tech stack to use. I've spent three months researching instead of guiding trips." |
| Current workaround | Manual research, generic templates (Canva, Shopify), expensive consultants ($5-10K), or give up and take a job |
| What progress looks like | Operating business within a week, focused on domain expertise. First customer within a month. Never had to choose a tech stack. |
| Hidden objection | "Will this platform lock me in? Can a machine really understand my craft? What if the generated business plan is generic and wrong?" |
| Switch trigger | Getting a real customer through the generated pipeline — proof that the machine-built infrastructure actually works for their specific domain |
Features that serve this job: #1, #2, #3, #4, #5, #6, #7, #8
Job 2: Platform Engineer Creates Venture for Client
Situation: A builder wants to help someone launch a business on the mycelium but infrastructure wiring takes weeks of commodity work.
| Element | Detail |
|---|---|
| Struggling moment | "I spend 80% of my time on commodity infrastructure — the same CRM, the same payments, the same auth. Only 20% goes to what makes this venture unique." |
| Current workaround | Copy-paste from last venture, manually adapt, fix what breaks |
| What progress looks like | One command generates the base. Engineer customizes the domain-specific 20% in days, not weeks. |
| Hidden objection | "What if the generated base doesn't fit this client's domain? What if I spend more time fixing the generator's output than I would building from scratch?" |
| Switch trigger | Second venture builds faster than the first — proof that the generator compounds rather than fragments |
Features that serve this job: #6, #7, #9, #10
Example Pairs
| Quality | Input | Expected Output | Why This Score |
|---|---|---|---|
| Excellent | "I run fishing charters, Hauraki Gulf, Auckland. Tourists can't find reliable local guides." | Market report (3 competitors, tourism stats, gap analysis) + value prop ("Verified local guide matching for Hauraki Gulf tourists") + 3 ICP segments (international tourists, corporate team-building, family holidays) + operations templates + sales pipeline | Clear skill, clear domain, clear location, clear friction. All algorithms have strong signal. |
| Acceptable | "I'm good at building things. Auckland area." | Clarification prompt: "What kind of building? (construction, software, furniture, etc.)" → refined input → abbreviated research with low-confidence flag | Ambiguous skill needs refinement. System handles gracefully but output quality lower. |
| Unacceptable | "I want to make money fast" | VFL refusal: "This doesn't describe a craft or skill. The generator creates businesses around real expertise. What are you genuinely good at?" | No skill, no domain, extractive intent. VFL scorer rejects. |
Role Definitions
| Role | Access | Permissions |
|---|---|---|
| Founder | Their venture only | Submit ideas, view research, approve/reject value prop, launch venture, manage operations |
| Platform Engineer | All ventures they manage | Create ventures for clients, customize templates, configure agent workforce |
| Admin | All ventures on platform | Monitor compound rate, manage algorithms, review refusals, adjust thresholds |
Venture as Universal Work Unit
A venture isn't just "a company." It's the universal unit of scoped work at any scale:
| Example | What the Generator Produces | Parent Venture |
|---|---|---|
| "I run fishing charters" | Full operating business on the mycelium | None (top-level) |
| "Win the Fonterra RFP" | Scoped project with deadline, WorkCharts, deliverables | prettymint |
| "Launch summer marketing campaign" | Campaign with content calendar, channel strategy, metrics | berleytrails |
| "Onboard new enterprise client" | Workflow with checklist, timeline, success criteria | stackmates |
The generator adapts to scale. parentVentureId in the schema enables nesting.
Relationship to Other PRDs
| PRD | Relationship | Data Flow |
|---|---|---|
| A2A Sprout Generator | Parent/Foundation | BIG calls Sprout to create infrastructure. Sprout handles the 18-table technical substrate. BIG adds business intelligence on top. |
| Agent Platform | Peer | BIG registers agent cards that Agent Platform manages. Agent workforce (Phase 3) depends on Agent Platform capabilities. |
| Intelligence Functions | Component | BIG consumes all 9 algorithms. Intelligence Functions provides the portable decision logic. |
| Sales CRM & RFP | Peer/Consumer | Generated ventures use CRM for sales pipeline. BIG seeds venture_contacts and sales structures. |
| ETL Data Tool | Peer | BIG uses ETL pipelines for domain research data ingestion and venture data population. |
Context
- Pictures — Pre-flight maps that feed this spec
- Prompt Deck — Sales compression of this spec
- A2A Sprout Generator — Infrastructure layer spec
- Business Development — The framework this generator automates
- Ventures — The factory's output: 7 ventures waiting for this generator
- WorkCharts — Execution layer the generator seeds with ventureId
- AI Product Requirements — Section definitions