RFQs are one of the most important levers in Procurement, but at the same time they are often one of the biggest time wasters. Manual setup, scattered data, inconsistent bid formats, and time-consuming comparisons mean that benchmarking happens less often than it should. In this webinar, Torben Hinrichs and Jakob Hafner show how AI-powered agents simplify the entire RFQ process: from identifying potential to evaluating bids.

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Starting point – The manual RFQ process slows Procurement

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In many purchasing departments, the RFQ process is still the same as it was years ago. Items have to be exported from the ERP system, relevant suppliers are searched for individually, and requests are prepared in Word, Excel, or email. Quotes come back in completely different formats and have to be compared manually.

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Each round takes time. Each adjustment means copying, formatting, and updating again. Often, some of the information is stored in files, some in mailboxes, and some in personal notes. As a result, it takes a long time to complete a full comparison and make a decision.

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This way of working means that RFQs are known to be an effective tool, but are often used less than they should be in everyday business. The effort involved is simply too great, especially when multiple suppliers or multiple rounds are planned.

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Many purchasing departments would therefore like to generate more competition and benchmark more frequently, but rarely manage to do so in their day-to-day business because the preparation ties up too much capacity. This is exactly where the webinar comes in.

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How AI simplifies and accelerates the RFQ process

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Once it becomes clear how time-consuming traditional RFQs are, it becomes obvious where modern Procurement can Procurement . Many of the steps that are currently performed manually follow recurring patterns: selecting items, checking suppliers, compiling request data, obtaining quotes, and then laboriously comparing them. It is precisely in these areas that AI can provide support and ensure that Procurement gains Procurement time for evaluation and strategy.

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Instead of starting from scratch every time, the system automatically analyzes relevant price trends, contract data, and item movements. This provides early indications of which products might be suitable for benchmarking or which inquiries are coming up soon. Only then do the AI agents come into play: they take on many of the preparatory and comparative tasks that make the RFQ process so time-consuming today.

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An overview of the most important agents:

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Automatic notifications - The system automatically identifies suitable starting points for RFQs, for example in the event of unusual price developments or upcoming contract deadlines.

RFQ Agent - A complete RFQ is generated from an identified topic with a single click. Items, suppliers, and relevant data points are prepared directly.

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Offer selection agent - Suppliers can submit offers in any format. The AI automatically reads the content and converts it into a uniform schema.

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Comparison logic agent - All responses are automatically compared. Differences in prices, quantities, or conditions are clearly highlighted without manual table maintenance.

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This creates an RFQ process that is structured, significantly faster, and completely traceable. Procurement time for analysis and argumentation, while AI takes over repetitive tasks in the background.

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From the notice to the decision

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As soon as a relevant price trend or deviation is detected, Procurement can go through Procurement entire RFQ process without media discontinuity. With just a few clicks, a complete request can be generated from a notification, and offers are automatically read and compared in a uniform structure. If necessary, a further round can be initiated immediately.

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What used to require numerous tools, spreadsheets, and email exchanges can now be accomplished in a single, continuous process. This not only makes benchmarking faster, but also easier to scale: more suppliers, clearer comparisons, and decisions based on clean data.

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Outlook

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The next steps show where the RFQ process is headed. Features such as multi-stage bidding rounds, structured queries to suppliers, and integrated savings tracking ensure that not only is the creation of RFQs faster, but so is the entire follow-up process. In the future, AI agents will be able to provide even greater support by suggesting suitable times for new requests or automatically prioritizing anomalies. This turns individual benchmarks into a continuous, data-based process.

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Conclusion

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The RFQ process changes significantly when recurring steps are automated and all relevant data is available centrally. Notes are created earlier, requests are prepared in a matter of seconds, and quotes can be compared directly. This leaves more time for evaluation and strategy, while AI takes over repetitive tasks in the background and ensures end-to-end transparency.

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Torben Hinrichs and Jakob Hafner demonstrate how companies can use Tacto to implement RFQs more quickly, in a more structured and scalable manner, and why a higher tender rate directly leads to better decisions.