Rapidflare Launches Rapidbench, a Benchmark for AI Agents Answering Electronics Product Queries

In Rapidflare's first evaluation, tested models achieved pass rates 34 to 39 percentage points higher with access to structured catalog data than with web search.

SAN JOSE, Calif., Nov. 12, 2026 — Rapidflare today launched Rapidbench, a research benchmark designed to measure how accurately AI agents answer technical queries about electronics products.

AI is changing how electronics teams discover products, compare specifications and make selection decisions. But the information behind those decisions is often scattered across catalogs, datasheets and document revisions, where a small specification difference can change an answer. An agent must identify relevant products, interpret their specifications correctly and account for differences across sources. Electronics teams need a way to assess whether an AI agent can do that reliably.

Electronics teams need benchmarks that reflect the technical queries their AI agents are expected to answer, Rapidbench is designed to help fill that gap with a domain-specific way to evaluate performance.

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"Electronics teams need benchmarks that reflect the technical queries their AI agents are expected to answer. General-purpose benchmarks measure broad capabilities, but don't show how well an agent handles queries across complex electronics catalogs. Rapidbench is designed to help fill that gap with a domain-specific way to evaluate performance," said Navanee Sundaramoorthy, CEO of Rapidflare.

Rapidbench's first version evaluates AI agents on 116 technical product queries involving NVIDIA's Jetson and IGX platforms and partner products. The test catalog covers 2,130 products from 212 manufacturers across 54 categories. Rapidflare compared agents without external tools, with web search, and with access to its structured catalog through the Model Context Protocol (MCP). Across three runs, the tested models achieved pass rates of about 18% without tools, 49% to 60% with web search, and 88% to 94% with access to Rapidflare's structured catalog via MCP. These results suggest that structured product intelligence can improve agent performance on complex electronics queries within the scope of this evaluation.

Rapidflare generated the query set from its product catalog, guided by real search trends. Each query was validated against the catalog for answerability and reviewed by human evaluators for realism and correctness, and responses were graded against catalog-backed factual checks for correctness, completeness and clarity. Rapidflare ran each setup three times against a fixed catalog snapshot. Human reviewers agreed with the automated judge's pass/fail decisions on 98% of the reviewed subset.

The first version is limited to NVIDIA's Jetson and IGX ecosystem and 116 queries. Rapidflare has published the methodology, limitations and evaluation trajectories so readers can examine how the results were produced.

"Building Rapidbench required a reliable reference for what counts as a correct answer. We verified product data against manufacturer sources, reconciled conflicting specifications and tied each query to explicit, checkable facts. The published results include the evidence and answer traces, so readers can examine how each setup performed and where it fell short," said Vasanth Asokan, CTO of Rapidflare.

Rapidbench is available at https://www.rapidflare.ai/resources/rapidbench. The page includes the results, methodology, limitations and agent trajectories.

Media Contact

Balpreet Kaur, Rapidflare, 1 2068614231, [email protected], rapidflare.ai

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SOURCE Rapidflare