Rapidflare suite

Rapidflare builds AI agents for technical sales and support in electronics. The agents select the correct parts, compare them to the competition, write proposals, and answer support questions, running on one knowledge graph underneath with five task-specific agents on top. A selection agent narrows thousands of parts to the three that work by asking the clarifying questions a senior application engineer would ask, and a comparison agent reads both catalogs and builds a side by side, spec by spec, with a source on every row.

Accuracy is the stated bar: the homepage says a general model pointed at a folder of PDFs will guess, and that on a parametric spec, improvisation is a wrong part in a customer's hands. The system is built on a curated knowledge graph from the vendor's datasheets, catalogs, BOMs, and support history, with structured reasoning that pulls only the knowledge the task needs, expert-in-the-loop validation by the vendor's application engineers, and a source on every line so every claim traces to the document it came from.

The homepage reports document extraction accuracy of 95 percent for Rapidflare in 2026, against 49 percent for AWS Textract and 62 percent for Contextual AI, and states that a forward-deployed engineering team builds the solution with the customer with continuous accuracy evaluations so quality is measured, not assumed. A reprinted Rapidflare blog post describes a three-tier safety filter that runs in parallel with the context pipeline and reports that 100 percent of harmful and off-topic queries were correctly handled against the ToxiGen benchmark, with 98.5 percent blocked at the input level in under one second. The homepage's own impact figures, including 80 percent of quotes sent with zero manual rework and a 30 percent reduction in L1 support tickets, are vendor-reported and are stated here as the vendor's claims, not as independently verified results.