DEEPX put its SDK, repositories, and engineering docs behind one answer layer
The NPU maker unified DXNN SDK documentation, GitHub repositories, and internal engineering systems into one cited answer layer for edge AI developers.
The knowledge existed, it was just spread across four kinds of system
Deploying AI silicon is not one question, it is a chain of them: which model format the toolchain accepts, how to quantize for the target, what the SDK expects, why a build fails on one board and not another. DEEPX had answers to all of it. They were spread across GitHub repositories, DXNN SDK documentation, developer portals, tutorials, and internal support knowledge. An engineer mid integration had to know which of those systems held the answer before they could start looking. As the DEEPX ecosystem grew across robotics, smart mobility, industrial automation, and intelligent infrastructure, that lookup cost grew with every new developer.
One intelligence layer over the whole developer surface
Rapidflare ingested the technical parameters of the products themselves, schematics, configuration guides, and maintenance logs, alongside the SDK documentation and the repositories. The platform reasons over the hardware and the software together rather than treating each document as a separate lookup, using knowledge graphs, structured reasoning with tool calling, and source level traceability. Every answer carries a deep link to the document it came from, which is the part that matters in mission critical work: an engineer can check the claim rather than trust it.
Live across support, engineering, and developer enablement
The platform is live and integrated across three DEEPX systems: technical support, engineering, and developer enablement. Developers, customers, and DEEPX's own engineers get instant answers cited across what used to be separate knowledge sources. Before rolling it out, DEEPX benchmarked Rapidflare's accuracy internally against the layered questions that come with deploying new AI silicon at scale. The roadmap runs toward the edge itself: automated troubleshooting, self diagnosis, and configuration guidance on factory robots and autonomous machinery.
Why silicon makes this harder
A camera or a sensor has a spec sheet. An NPU has a spec sheet, a toolchain, a runtime, a set of supported model formats, quantization behavior that varies by target, and a board bring-up path. The question “will this work” decomposes into a dozen smaller ones, and the answers sit at different altitudes: some in silicon documentation, some in SDK reference, some in a repository issue thread. That is the shape of the problem Rapidflare was built for, and it is why DEEPX tested accuracy before scale.
The citation is the product
DEEPX’s own framing is that trust is the constraint, not capability. An answer an engineer cannot verify is worse than no answer, because it costs them the time to discover it was wrong. Every response the layer returns is cited with a deep link into the source, so verification takes one click.
Physical AI systems will increasingly depend on trusted knowledge as much as they depend on compute. Our work with DEEPX demonstrates how organizations can create a reliable intelligence layer.
Prush Palanichamy, Co-Founder and CRO, Rapidflare
Where it goes next
Both companies are working toward closing the loop between documentation and what happens in the field, so operational feedback from deployed systems feeds back into the knowledge layer. The end state is edge devices, factory robots and autonomous machinery, running automated troubleshooting and self diagnosis locally.
“As our ecosystem grows, developers and customers need fast, reliable access to the knowledge required to build, optimize, and deploy on DEEPX. Rapidflare enables us to deliver that experience while maintaining the accuracy, traceability, and trust required in mission-critical technical environments.”