The Pricing Problem in Construction
Accurate BOQ pricing has historically required weeks of manual effort — cross-referencing supplier catalogues, requesting quotations, and reconciling currency fluctuations. AI changes this equation fundamentally.
How Qimta's AI Engine Works
The Qimta pricing engine uses a Retrieval-Augmented Generation (RAG) model trained on three years of regional transaction data. Given a BOQ line item, it retrieves the top five supplier offers matching the exact specification, then ranks them by total landed cost including delivery, VAT, and compliance fees.
Accuracy Benchmarks
In independent testing across 47 completed projects, Qimta's AI pricing achieved a 94.7% accuracy rate against final project cost — outperforming the industry average of 78.2% for manual estimation.
Continuous Learning
Every completed transaction feeds back into the model, improving price predictions for similar items in subsequent BOQs. Projects executed through Qimta benefit from an ever-improving pricing baseline.
Human-in-the-Loop Controls
Procurement managers retain full override capability. Any AI-suggested price can be manually adjusted, and the reason for adjustment is captured for future model training.