End-to-end AI sales cycle for an engineering contractor (Enterprise RAG)

Knowledge in a system, not in people's heads

At an engineering contractor, quote quality depends on engineers' experience, scattered Excel sheets and email threads. The goal was a system that tracks the full lifecycle in a database: incoming request, missing documents, costing, contract, procurement, certificate of completion, closure.

Technical implementation and business impact

RAG-based knowledge base and costing engine

The core of the system is a vectorised knowledge base. It holds the methodology for selecting the technical solution, the list of services and resources, and the pricing logic. The costing Excel sheets are incorporated dynamically as tools of an agent-based system. The output is a PDF quote, reference calculations and a draft contract.

Agents under human control

Every critical point has a human approval. The suggested priority and deadline can be adjusted in a chat interface, an engineer approves the quote alongside the reference calculations, and a manager approves the procurement list. A gap-check agent recognises which documents are missing for a given project type and requests them from the client with staged, automatic follow-ups.

Measurable business impact and ROI

Requests for quote no longer get lost in the inbox. Every lead enters the CRM with a structured data sheet, a priority and a deadline. Daily documentation during delivery automatically produces certificates of completion and draft partial invoices, which directly shortens the billing cycle and improves days sales outstanding (DSO).

Integration and auditability

The system connects to the existing CRM, email, warehouse management software and ERP. Every agent decision is logged and traceable. At project closure, the plan-vs-actual cost, time and margin analysis goes into a KPI database, so every closed project improves the accuracy of the next quote.