Your product knowledge shouldn’t depend on one application engineer’s calendar
Rapidflare gives manufacturers a product selection experience that behaves like a sales engineer: understanding a high-level use case, asking the right technical questions, and arriving at a part number with the reasoning shown.
Parametric search only covers a sliver of the spec
It works when the customer already knows the parameters, or knows clearly what they want. It goes silent the moment someone describes a use case in natural language, an application, not a specification, which is exactly where the hard, valuable questions live.
- "I need a component with these specific parameters." Your existing product selector handles this well.
- "I’m designing an automotive infotainment system. What should I use?" Nothing on your site can answer this. It requires contextual technical reasoning.
- So the customer opens a ticket, emails a rep, or fills in a contact form and waits.
- Your product catalog holds a subset of the technical knowledge that lives in datasheets, application notes and reference designs, which can get complex.
- Answering properly means pulling in a sales engineer, an application engineer or a product specialist.
- Long-tail customers never get that attention at all, so the design win goes to whoever answered first.
Someone at your company can already answer almost any application question. The problem is there’s one of them, and every inquiry queues behind the same few people.
An agent that reasons like your best application engineer
It asks the clarifying questions a senior application engineer would ask, then narrows thousands of products down to the ones that fit.
Guided product selection
The agent takes a high-level use case, asks the relevant technical questions, narrows the requirement, and reasons across your product information to identify suitable parts, then explains why, then cites its source inline.
When an answer relies on a document you own, Rapidflare cites that document directly. And when the answer depends on a diagram embedded in a PDF, our Visual Reasoning Engine understands the diagram itself, rather than discarding it during ingestion.
Technical support
Register maps, pinouts, power sequencing, SDK integration, hardware and software bring-up: these are the questions your FAEs repeatedly answer, and every one of them is already documented somewhere.
Deployed on your docs site, in your developer community, or inside your product. In the AMD ROCm Discord and Discourse community it cut L1 support requests by more than 30%.
Channel and partner enablement
The same agent, pointed at your distributors, channel partners and resellers, so partners can answer for your products as well as your own team does, instead of defaulting to whichever line they know best.
One live example: a manufacturer’s agents running inside a distributor’s own sales team, day to day, answering questions their reps would otherwise escalate back to the factory.
Competitive comparison and cross-reference
Customers ask this unprompted. In live conversation logs, we see queries like “What’s your equivalent to this competitor’s part?” The agent reads both catalogs side by side, shortlists the closest matches, and builds the spec-by-spec comparison.
The same capability lets your own reps defend a socket without waiting for a competitive analysis.
Technical proposals
Once the parts are selected, the agent generates the technical recommendation or proposal the customer can act on, assembled from your own documentation rather than a template.
Three ways manufacturers deploy it
AMD runs all three.
Internal enablement
Your own sales teams, application engineers, product specialists and support teams, behind single sign-on, with access to internal and pre-release material the public agent never sees.
Partner enablement
Provided to your distributors, channel partners and resellers so they can sell your products with the technical depth of your own team.
Website and long-tail customers
Deployed on your public site, so customers find the right product without a human sales engineer in the loop for every inquiry, including the long-tail customers who would never have gotten that engineer’s attention at all.
What moves
- Time to technical response
- Application-engineering workload and dependency
- Product discovery and website conversion
- Qualified opportunities created
- Design-win velocity
- Channel partner productivity
- Shadow production testing first. Two weeks running against real Discord and forum questions, with responses monitored and ranked internally before anything went live.
- An admin dashboard ran alongside it. Their team reviewed conversations and performance analytics throughout.
- Then expansion by surface: guided selection for embedded and FPGA, support agents for FPGA and x86 embedded processors behind AMD single sign-on, and the ROCm developer community.
Taoglas and Qorvo run Rapidflare on their public sites. Critical Link runs it in the site search bar. AMD runs it across four separate deployments including the ROCm developer community.
Common questions from manufacturers
We already have a parametric product selector. Why add this?
Parametric search is a filter: it needs the customer to already know the parameters. Rapidflare handles the case where the customer knows their application but not the specification, which is the inquiry that currently goes to an application engineer. The two work together: most customers who can use your existing selector should keep using it.
What if our product catalog doesn’t contain everything in our datasheets?
That is the normal starting position. We ingest the datasheets, application notes, reference designs and support history alongside the catalog, and structure all of it into one knowledge graph, so the agent can reason over material that was never in the PIM.
What about specifications that only exist inside diagrams?
Conventional retrieval flattens documents into text at ingestion and discards the diagrams, which means specifications inside schematics, timing charts and mechanical drawings never reach retrieval at all. Our Visual Reasoning Engine reconstructs and indexes those visuals so the agent can reason over them directly.
How do we know it is not making things up?
Every output is traceable to the document it came from, so your engineers can open the source and check. Beyond that, we run a practical evaluation framework against real traffic, and we typically start customers in a shadow mode where the team reviews responses before anything is exposed.
Can our channel partners use it?
Yes. You can provision the agent to distributors and resellers so they answer for your products with your technical depth, which directly affects whether a partner leads with your line or someone else’s.
See it on your own products
Send us your public documentation and we’ll build a working selection agent on your catalog before the first call.
SOC 2 Type II · Every answer traceable to its source