| This guide outlines a lean, high-velocity technical implementation for your **Tender-Winning Engine**, pivoting from a directory to an AI-powered execution platform. The goal is to deploy a functional "Pipeline" (Upload → Extract → Generate → Charge) within **30 days**. | |
| ### **1. High-Level System Architecture** | |
| The architecture follows the **2026 AI SaaS MVP standard**, prioritizing speed of delivery over complex infrastructure. | |
| * **Frontend:** **Next.js 15**. It provides Server-Side Rendering (SSR) for fast performance on weak mobile data connections common in Kenyan county offices. | |
| * **Backend:** A single **Node.js or Python (FastAPI)** backend in a monorepo for simplicity. | |
| * **Database & Auth:** **Supabase (PostgreSQL + pgvector)**. This handles relational data, authentication, and vector storage for RAG (Retrieval-Augmented Generation) in one place. | |
| * **File Storage:** **AWS S3** or Supabase Storage for storing original RFP PDFs and generated proposal drafts. | |
| * **AI Engine:** **Claude 3.5 Sonnet API**. Claude is specifically recommended for its superior ability to reason over long, complex legal clauses and tables found in Kenyan government RFPs. | |
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| ### **2. Document Processing Pipeline (The "Brain")** | |
| This is your primary technical moat. You must handle the "scanned garbage" PDFs often found on government portals. | |
| 1. **Extraction Layer:** | |
| * Use **pdfplumber** for clean, digital-first PDFs. | |
| * Use **Tesseract OCR** as a fallback for scanned documents. | |
| 2. **Preprocessing:** Normalize text by removing headers/footers and splitting documents into logical sections (Preliminary, Technical, Financial). | |
| 3. **Structure Detection:** Prompt the LLM to output **STRICT JSON**. Identify mandatory documents (Bid bonds, Tax certificates, NCA levels), evaluation criteria, and deadlines. | |
| 4. **RAG Layer:** Store extracted requirements in **pgvector**. This allows the AI to anchor the generated proposal text to the specific RFP content, preventing hallucinations. | |
| --- | |
| ### **3. Database Schema (PostgreSQL)** | |
| Keep the initial schema minimal to support the core "Job" workflow. | |
| * **Users Table:** `id`, `email`, `plan_type` (Free, Pro, White-label), `company_profile` (JSON for AGPO status, NCA category, etc.). | |
| * **Jobs Table:** `id`, `user_id`, `file_url`, `status` (Processing, Completed), `created_at`. | |
| * **Results Table:** `job_id`, `structured_output` (JSON of requirements), `proposal_draft_text`, `risk_score`. | |
| --- | |
| ### **4. Payment Integration (Revenue Reality)** | |
| For the Kenyan market, **M-Pesa Daraja 3.0 API** is non-negotiable. | |
| * **Workflow:** User uploads PDF → AI extracts basic metadata → User hits "Unlock Full Analysis/Draft" → M-Pesa STK Push (KES 999 or KES 4,999) → Webhook updates `plan_type` or `job_status`. | |
| * **Conversion:** Native M-Pesa integration yields significantly higher conversion than card-only systems like Stripe in Kenya. | |
| --- | |
| ### **5. 30-Day Build & Deployment Plan** | |
| Deploy in **days, not weeks** to validate demand manually before scaling. | |
| * **Week 1 (Infrastructure):** Setup Next.js + Supabase; build the PDF upload UI and S3 storage. | |
| * **Week 2 (AI Core):** Implement the PDF-to-JSON extraction pipeline using Claude 3.5. Refine system prompts for Kenyan procurement law (PPADA 2015). | |
| * **Week 3 (Output):** Build the **Bid/No-Bid Scorer** and the proposal drafting engine. Integrate M-Pesa callbacks. | |
| * **Week 4 (Launch):** Deploy to **Vercel** (Frontend) and **HF Spaces** (Backend). Hand-sell to 10–20 contractors via WhatsApp groups. | |
| --- | |
| ### **6. Critical Technical Risks to Mitigate** | |
| * **Garbage In, Garbage Out:** Kenyan tenders are often poor quality. Your prompt engineering must be robust, and OCR fallbacks are mandatory from day one. | |
| * **Data Scraper Hell:** Do **not** start by building scrapers for dozens of portals. Let users upload the PDFs they already have; focus engineering on *processing* the document, not finding it. | |
| * **Security:** If you pursue the **White-label tier** for consultants, multi-tenancy and strict data isolation between different consulting firms are required to prevent data leaks. |