Qimta — BOQ AI Pricing Engine
B2B construction pricing platform for Saudi Arabia & GCC — an AI model powered by RAG extracts BOQ line quantities, matches 19,699+ verified products from manufacturer specs, and continuously self-trains through web scraping pipelines to stay current with market pricing.
The RAG model continuously scrapes manufacturer websites, catalogs, and price databases to update its knowledge base automatically — keeping pricing accuracy above 99.9% without human intervention.
Project Overview
Qimta is a B2B SaaS platform that solves one of the biggest bottlenecks in construction procurement: pricing every BOQ (Bill of Quantities) line accurately and instantly. Traditional pricing requires days of manual research across supplier catalogs — Qimta's AI does it in under 60 seconds.
I contributed to the backend data pipeline and AI model infrastructure — building web scraping modules, data normalization pipelines, and PostgreSQL schemas for storing and querying the product knowledge base that powers the RAG engine.
AI Pipeline Architecture
Key Features & Contributions
Results & Scale
Technical Challenges
Many manufacturers use JavaScript-heavy product catalogs, rate limiting, and anti-bot measures. Solved with headless browser scraping (Playwright/Selenium), rotating proxies, and respectful rate-limiting with exponential backoff.
Same product scraped from multiple sources often had inconsistent naming, unit differences, and spec variations. Built a fuzzy matching + embedding similarity pipeline to deduplicate and canonicalize product records before storage.
Full model retraining on every price update would be prohibitively expensive. Solved using RAG architecture — the retrieval layer updates its vector index incrementally on new/changed products, while the LLM component remains static, giving fresh pricing without retraining costs.