This case study shows how we built a GenAI RfP processing solution on Azure that cut matching time by 3x while keeping human experts in control.
The Challenge
A single RfP can include dozens of documents: specifications, drawings, forms and clarifications, all referencing each other, easily exceeding 100 pages. The relevant information is rarely in one place and the structure varies widely depending on whether the client is a small municipality or a large corporation, operating in different countries with different standards and languages.
Interpretation starts before any matching can begin. What a tender calls one thing, an internal product catalog may call something entirely different. Measurements are rarely consistent either: the same dimension can appear as cm, meters, inches or pieces within the same document. Resolving these mismatches requires expertise, and expertise takes time.
Mapping each tender position to the right product requires matching a dozen technical details at once. Information can be missing, incorrect, or only revealed through direct customer conversations.
Standard Copilot tools were not built for this kind of work. They can process text, but they cannot reliably handle the domain-specific complexity that comes with real procurement. And the stakes make that gap matter: offers are legally binding. A wrong product mapping can lead to financial and reputational damage
That is why human judgment cannot be removed from this process. The question was never whether to keep experts involved. It was how to give them a tool that handles the complexity so they can focus on the decisions that require their expertise.
What We Built
Together with our customer we developed a custom GenAI RfP processing solution on Azure, embedded directly into their existing sales software. The goal was not to replace the experts but to give them a tool that handles the heavy lifting: parsing RfP documents across dozens of files, resolving terminology mismatches through a domain-specific glossary, normalizing inconsistent units, and matching requirements against a complex product portfolio.
The system understands context, not just keywords. When a tender uses different terminology than the internal product catalog, the solution bridges that gap automatically. When units are inconsistent, it normalizes them before matching begins. And for every product suggestion it makes, it surfaces the reasoning behind it so the expert can follow the logic, challenge it if needed, and make an informed decision.
Because offers are legally binding, every recommendation comes with a confidence score and a full audit trail. Experts review and approve before anything goes out. The AI handles the complexity, the expert makes the call.
On the technical side, the solution was built entirely on Azure and the underlying GenAI model was carefully tuned for cost efficiency. Processing large volumes of documents means token costs can escalate quickly.
The Result
The GenAI RfP processing is now up to 3x faster. What previously took days now takes hours. The team spends less time on manual document analysis and more time on the decisions that require human judgment: edge cases, customer clarifications and final validation.
Speed was only part of it. The solution also brought consistency. Every tender is processed with the same logic, the same terminology resolution and the same audit trail. For a team handling legally binding offers, that consistency is not just a process benefit, it is a risk reduction.
Instead of spending hours reading through documents and manually cross-referencing product catalogs, the team can now focus on the cases where their knowledge and judgment matter.
