Intelligent RDA

The approach we've developed shifts the entry point of the process: instead of requiring the operator to structure the request before it can be processed, the system adapts to the client's request format.

An artificial intelligence engine analyzes the received text — whether it's an email, an attached document, or a message — and autonomously extracts relevant information: what is requested, in what quantity, with what technical specifications, and for what application. This information is then compared with the product catalog and stock availability, generating a configuration proposal ready for the operator's review.

The operator doesn't disappear from the process; their role changes. Instead of building the proposal from scratch, they validate it, correct it if necessary, and approve it. The time previously needed to process a request is significantly reduced, and the quality of the proposal improves because the system has already verified availability and catalog matching.

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What concretely changes for the company

The most immediate benefit is the speed of response to the client. A company that can send a quote within hours of receiving a request — rather than the next day or later — gains a concrete competitive advantage, especially in markets where clients evaluate multiple suppliers simultaneously.

The second benefit is error reduction. When the configuration is automatically built from the request text and verified against the actual catalog, transcription errors and proposals with unavailable items become exceptions rather than frequent occurrences.

The third benefit, often underestimated, is the Unlocking capacity. People who currently spend hours processing requests can dedicate that time to customer relationships, negotiation, and opportunity analysis. The sales team's value is expressed in what truly matters.

There is no one-size-fits-all solution. The starting point is always an analysis of the existing process: how requests arrive, their format, how they are currently processed, and where bottlenecks are concentrated.

From this analysis, a tailor-made architecture is defined, which takes into account existing systems — ERP, management software, product archive — and integrates with them without requiring replacements. The AI engine is trained and calibrated on the company's specific technical vocabulary, ensuring an appropriate level of accuracy from the outset for the operational context.

The typical process involves an initial analysis and prototyping phase, followed by a pilot on a real subset of requests, and then progressive extension to the full workflow.

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Want to see if this approach applies to your process?
The BlueIT team is available for a direct discussion about your specific context.