Describe your system in plain English. RoboSpec lays out the architecture, then grounds every component in real distributor and manufacturer data — with calibrated confidence and full source provenance.
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Compact vision sensor for part presence and orientation before pick. Matches the required resolution and monochrome imaging for this cell.
One plain-English box — what it does, payload, throughput, environment. No forms to fill.
Get a suggested part list first. Uncheck anything you don't need and adjust quantities — with live power-distribution and communication guidance updating as you go. Then generate the architecture.
A clickable diagram in seconds — every component as a node, wired with real connection types: EtherNet/IP, safety bus, IO-Link.
Click any node for its reasoning, confidence, and sourcing — manufacturer-verified, distributor-verified, or AI-suggested. Export the BOM as .csv.
Your whole cell as connected nodes — controller, robot, tooling, vision, safety, conveyor, sensors, power. Hover a wire to see its protocol.
Every recommendation is grounded in live distributor and manufacturer data — never an invented model number. Your pick is never silently swapped: if it can't be verified it's labeled AI-suggested, with up to two real alternatives to choose from.
HIGH / MEDIUM / LOW, derived from the data itself. A missing spec caps confidence — it's never inflated.
Every part is tagged manufacturer-verified, distributor-verified, or AI-suggested — with the requirement it was matched against. You always know where a number came from.
Per-part price and lead time, risk flags surfaced up front, plus a rough total system cost range for the whole cell.
Export one component or the whole system — brand, part number, confidence, price, and a lookup link for every line.
Part is active, in stock, and every spec you gave is confirmed against real distributor or manufacturer data. Safe to source.
A real part, but one or more specs couldn't be fully verified, or it's obsolete or out of stock. Capped here on purpose.
No verified match — the suggestion is AI-inferred and flagged as a starting point to confirm before sourcing. Honest by default.
A deterministic validation layer scores each part against your specs and live distributor & manufacturer data — the confidence you see is derived from that check, not the model's opinion of its own answer.
The moat isn't generating a spec — it's output you can trust. Real part numbers, honest confidence, a clear record of where every recommendation came from, and no incentive to favor whoever pays to be listed.
Turn a client brief into a full architecture and a real, sourceable BOM in minutes — with confidence flags that tell you exactly what still needs a phone call.
Scope new cells fast and reach procurement with verified, in-stock part numbers instead of a rough estimate a generic AI tool may have made up.
Get a structured spec with provenance on every line — so you know which numbers are catalog-verified and which are still a starting point.
Describe what you're building and let the model reason out the architecture — then grounded part data cuts the hours you'd otherwise spend searching for each component yourself.
Today, RoboSpec owns the spec-and-decision layer — the trusted parts and BOM at the foundation of a build. From there it expands outward: the AI sandbox engineers live in across the whole cycle, and — in the process — the un-scrapable dataset of how hardware actually gets designed.
Expand from spec and BOM across the whole cycle — architecture, CAD layout, simulation, procurement — by orchestrating the tools engineers already use (SolidWorks, PLM, simulators), not rebuilding them. One sandbox, end to end.
Normalize parts onto ECLASS / ETIM and read interoperability files (EDS / ESI / GSD, OPC UA) to answer "what's the Allen-Bradley equivalent?" and "will these actually talk to each other?" — real evidence, not catalog adjacency.
Every Keep / Swap / Drop sharpens the next recommendation — a proprietary record of how engineers actually choose parts, so accuracy and confidence improve the more the tool is used.
Over time that record becomes the scarce, un-scrapable corpus of how physical hardware gets designed — the foundation for AI that can reason about the real world, not just code.
Try the live beta, or join the list for early access.
Free during beta · No credit card · No spam