Customer success story
AI Bid Prioritization for an Industrial Group
Scored bid requests show the commercial team which bids to work on first
The commercial team at a global industrial and technology group needed to decide sooner which requests for quotation (RFQs) to pursue. Roboyo built a generative AI application that reads incoming RFQs against product-specific criteria and scores them, with references to the documents behind each score. The team starts its own assessment from there.
15hours
reported time saved per bid
9%
more bid response capacity
€8million
reported top-line impact
- Client
- Anonymous industrial and technology group
- Process
- RFQ assessment and bid prioritization
- Technology
- Retrieval-augmented generation and low-code
The challenge
In one product category, the commercial team received a high volume of RFQs and invitations to bid. A response took about 180 hours to prepare on average. With no consistent way to tell early which requests fit best, the team could spend those hours on weak opportunities and miss bids it might have won.
The goal was to screen each request sooner and cut the effort of understanding it, while experienced staff kept the final commercial decision.
What Roboyo built
Roboyo and the commercial team first defined the scoring criteria for one product category. Roboyo then built a case management application that takes in the RFQ documents and uses retrieval-augmented generation to extract the relevant requirements, check them against the product rules, score each criterion and summarize the evidence.
For each bid, the application compiles a scoring sheet with the document references and supporting text behind each score, generates follow-up questions and keeps the bid information in one place. Commercial users review the detail and decide whether to pursue or decline. The application does not make that decision.
The result
The client reports an average of 15 hours saved per bid. With the same team, that is capacity for about 9% more bids. The win rate on submitted bids rose from 37% to 38%. Faster responses and better prioritization together delivered a reported €8 million in top-line impact in the first product category.
The team piloted the application for three months, then ran it in production for a further three months in that first category.
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