vibn-frontend/docs/PRODUCT_MARKET_FIT_ENGINE.md

Product-Market Fit (PMF) Engine

Vision: Vibn is not just a code generator; it is a "Business in a Box" platform. The PMF Engine bridges the gap between "Main Street" businesses (SMBs) and "Silicon Valley" SaaS by automating market research, lead generation, and Go-To-Market (GTM) strategy.

The Objective

When a user wants to build a product (e.g., "Software for Dentists"), the Vibn AI autonomously executes a complete End-to-End Discovery Pipeline. It guarantees the user receives:

1. Real Potential Customers: A qualified list of verified local businesses (with emails/phones) ready to be pitched.
2. Real Competitors: Identification of the proprietary SaaS incumbents currently dominating that specific niche.
3. Software Requirements (SRS): Database schemas and user flows extracted directly from proven open-source repositories in the same vertical.
4. An SEO Content Plan: Keyword gaps and blogging topics based on where competitors are overspending on Google Ads.
5. Website Positioning: Value propositions and wedge strategies designed explicitly to exploit competitor weaknesses.
6. Financials & Pricing: A calculated MRR model and disruptive pricing strategy based on local TAM and competitor costs.

1. Market Sizing & Lead Generation

Goal: Prove the market exists and give the founder their first 100 cold-outreach targets.

• Mechanism: The AI maps the software idea to a specific Google Business Profile category (e.g., gcid:dentist).
• Tooling: Uses the DataForSEO Business Listings API to scrape Google Maps in a defined geographic area.
• Output: A structured CSV/JSON of real-world businesses, including their names, addresses, ratings, and scraped email addresses.
• Data Co-op Model: Searches are charged via credits/micro-transactions. Results are cached in Vibn's Postgres database (market_leads). Over time, Vibn builds a proprietary, zero-cost database of every SMB in North America.

2. Competitor Identification & Website Teardown

Goal: Understand what the market leaders are doing and how to beat them.

• Mechanism: The AI identifies the top 3 proprietary SaaS competitors.
• Tooling: Natively uses http_fetch and browser_navigate (headless browser) to scrape competitor URLs.
• Output: Extracts their pricing model (or lack thereof), value propositions, feature sets, and website page hierarchy to inform the user's build plan.

3. SEO, Keywords & Ad Spend Analysis

Goal: Find the cheapest acquisition channels and keyword gaps.

• Mechanism: The AI analyzes the competitors' domains.
• Tooling: Uses DataForSEO Competitive Analysis / Keyword APIs (via the market_seo_analyze MCP tool).
• Output: Estimated monthly Google Ads spend, top-performing paid keywords, and low-difficulty organic keyword gaps (e.g., "open source dental booking widget").

4. Open Source Baselining & Architecture Extraction

Goal: Never start from scratch if a foundation already exists, and ensure the domain data models are accurate.

• Mechanism: The AI searches GitHub for actively maintained starter kits. It then explicitly reads the README and source code to reverse-engineer the "Software Requirements Specification" (SRS).
• Tooling: Uses the github_search and github_file MCP tools.
• Output: Extracts the exact database schemas (e.g., Camp Sessions, Parent Waivers, Cabin Assignments) and User Flows required to build a competitive product in this niche.

5. Automated Plan Generation

Goal: Turn all of this research into actionable engineering and marketing tasks.

• Mechanism: The AI acts as the user's product manager, writing the business plan directly into the Vibn platform's Plan Tab.
• Tooling: Uses plan_vision_set, plan_decision_log, and plan_task_add.
• Example Output:
  • Vision: "A $99/mo transparently priced patient engagement widget for dental clinics."
  • Decision: "Targeting 'booking widget' SEO gap instead of 'practice management'."
  • Tasks: Generated tickets for the AI to start scaffolding the Next.js landing page and database schema.

6. The Missing GTM & Operations Opportunities

To truly provide a "Business in a Box", the PMF Engine also synthesizes the following automatically:

• Compliance & Regulatory Flagging: (e.g., Identifying HIPAA/PIPEDA requirements for health-tech or SOC2 for fintech) and automatically injecting those requirements into the Build Plan.
• Financial Modeling & Pricing: Using the TAM count and competitor pricing to calculate exactly how many customers are needed to reach $10k MRR, and recommending a wedge pricing strategy.
• Integration Ecosystems: Identifying the existing tech stack of the leads (e.g., WordPress) and suggesting native plugins/widgets as an acquisition wedge.
• Customer Discovery & Sales Scripts: Generating tailored cold-email templates, starting with "Discovery Call" scripts for founders to validate the product before turning on hard-sales campaigns.
• Brand Identity Differentiation: Analyzing competitor aesthetics and generating a contrarian Design System (e.g., warm/consumer-focused vs. sterile/corporate) to save to the Design tab.

7. AI Credit Cost Estimation (The "Bill of Materials")

Goal: Give the user complete transparency on what it will cost to execute the entire plan.

• Mechanism: Before clicking "Build", the AI calculates the complexity of the generated plan (number of services, expected tool rounds, data enrichment API calls, and social media agent runs).
• Output: Presents a "Bill of Materials" detailing exactly how many Vibn AI Credits are required to scaffold, enrich, and ship the MVP, allowing the user to top-up their account before proceeding.