Every complex bid meant a sales manager chasing specs from engineers, prices from procurement and terms from legal, then stitching it together from old Word files. It took weeks, and the copy-paste bred errors: a stale price list that lost money, a document that went out still carrying the last client's name. By the time the proposal was ready, the window had often closed.
Preparing a proposal for a complex project was a multi-week relay across engineering, procurement and legal, and most of the actual work was searching old documents and pasting from past bids, the slowest and most error-prone way to build anything that matters.
That copy-paste habit carried real risk. Proposals went out with price lists that were already out of date and quietly lost the company money, and more than once a document reached a client still carrying the name of the previous one, the kind of mistake that costs credibility as well as the deal.
And it burned the wrong people. The pre-sales engineers were pulled off their real work again and again to answer the same technical questions for the sales team, so the delay in the proposal was also a tax on the company's scarcest technical talent.
Take the client's requirements, build the full document from live data, and route only the parts that need an expert.
The builder draws from the product-management and customer systems directly, so the proposal is assembled from live, authoritative data instead of whatever old Word file someone happened to copy.
The salesperson enters the client's basics, user counts, the infrastructure needed, the service level, and the AI builds a complete proposal of dozens of pages, with the exact technical specification, pricing and terms filled in.
The numbers are drawn from that day's supplier price lists rather than a spreadsheet from last quarter, so a proposal can no longer go out at a price that has already cost the company its margin.
The draft is never sent automatically. The system flags the clauses that need the chief engineer's or legal's eye and routes just those for a one-click sign-off, which removes the bottleneck without removing the check.
Because each document is generated fresh from the client's own inputs rather than edited from a previous bid, the embarrassing copy-paste errors, the wrong client name, the leftover clause, simply stop happening.
With the routine technical answers assembled automatically, the pre-sales engineers stop being a document-editing service for sales and go back to designing the complex architectures only they can.
A proposal out before the window closes, built on numbers that are actually current.
Preparing a proposal fell from 14 business days to 48 hours, which handed the company a decisive first-mover advantage over other integrators still assembling their bids by hand while the client waited.
Moving to automatic pricing off the core systems eliminated the pricing errors that used to send out unprofitable or stale bids, and freed the pre-sales engineers from document editing to focus on architecture and technical work in the field.
The win isn't a faster template; it's assembling the whole document from the source systems so the numbers are current, and routing only the risky clauses to an expert to approve. It is the same quote-from-real-numbers discipline behind our bid-pricing work, aimed at the full proposal document rather than the price alone.
A contractor priced tenders by hand and won work that lost money. AI bill-of-quantities pricing with a hard margin floor blocked loss-making bids and cut pricing from weeks to 48 hours.
Read the case → Procurement & spend · ManufacturingSpend sat in four ERPs and buying power was fragmented. AI unified and categorised all spend, read every contract against its invoices, and armed buyers with hard targets, cutting indirect spend 12%.
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