diff --git a/docs/PRODUCT_MARKET_FIT_ENGINE.md b/docs/PRODUCT_MARKET_FIT_ENGINE.md new file mode 100644 index 0000000..8e1c859 --- /dev/null +++ b/docs/PRODUCT_MARKET_FIT_ENGINE.md @@ -0,0 +1,40 @@ +# 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 scopes the market opportunity, designs the product architecture, and generates the initial customer list. + +## 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 +**Goal:** Never start from scratch if a foundation already exists. +* **Mechanism:** The AI searches GitHub for actively maintained, permissively licensed (MIT/Apache 2.0) starter kits. +* **Tooling:** Uses the `github_search` MCP tool. +* **Output:** A list of repositories the AI can immediately clone and modify for the user. + +## 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.