AI schematic review
Review revisions with your full design context — requirements, firmware, parts, and your own standards — and share a polished PDF report.
Explore schematic reviewElectronics is unforgiving — the harness is what makes AI safe for it. Circuitly connects your schematics, revisions, firmware, requirements, datasheets, and supply data into one loop where agents work and engineers decide. Start today with AI schematic review, grounded in that full context.
Three ways to start
Start with a concrete review or generation workflow, or connect a broader ECAD team around agent orchestration. Each path keeps engineers in control and the design record traceable.
Review revisions with your full design context — requirements, firmware, parts, and your own standards — and share a polished PDF report.
Explore schematic reviewGenerate reviewable native schematics from requirements, approved libraries, datasheets, and team standards.
Explore schematic generationOrchestrate agents across the tools, context, checks, and human approvals already inside your engineering system.
Explore the ECAD harnessUse cases
Each use case explains the engineering problem, connected workflow, useful output, and the decision that remains with the team.
Architecture, requirements coverage, potential issues, and open questions.
Open use caseReconstruct intent and dependencies before a respin or ownership change.
Open use caseCarry constraints, decisions, and open questions into the next workstream.
Open use caseA finished PCB is thousands of trade-offs across the whole product. The harness is the connective tissue: one loop agents can act on, constraints checked where they live, full traceability — so rapid iteration is safe and nothing is lost between tools.
one loop through the electronics lifecycle
Context, experiments, model choice, orchestration, guardrails — and none of it is electronics engineering. The harness absorbs that burden, so your team designs hardware instead of operating AI.
Electronics doesn't forgive "almost right." Every agent edit runs against your design rules, gets checked before merge, and lands as a reviewable diff. Precision is enforced by the harness — not hoped for from the model.
Which LLM reasons best over datasheets? When does a deterministic solver beat a model at routing? The harness runs that experimentation continuously — benchmarking LLMs and deterministic tools per electronics use case, re-tuning as they evolve. You never touch a model picker.
Schematic, layout, sourcing, supply risk, PLM, fab — each lives in its own tool, and context dies at every handoff. The harness is the connective layer: one workspace where context carries across the whole chain.
A chat window with MCP tools is still one loop — and one loop can’t hold a board. Circuitly’s agents run on tools that know electronics: what that sense resistor is doing in this circuit, what the datasheet allows, what industry standards expect — and merge their work into one reviewable change.
An LLM with tools and its own loop: it opens your files, runs a check, reads the result, and adjusts until the job is done. Without tools, a model can only talk about your design. With them, it can act on it — and verify what it did.
A focused agent spawned for one bounded piece of the job — verify this footprint against its datasheet, source this part, check this net. Small scope is the point: bounded work produces output you can actually check.
Splits a board-level change into parallel, bounded tasks, decides which agents run and in what order, and merges their work into one coherent change. One model doing everything at once is how context gets lost mid-schematic.
Everything around the agents that makes them usable and safe: the workspace they act in, tools, memory, guardrails, review, rollback. The layer you actually work at. This is Circuitly.
Why the layers matter: pin 4 either connects to pin 7 or it doesn't — but ten engineers will give you three defensible footprints for the same part. Electronics is precision and judgment side by side, so the harness runs two kinds of machinery.
Netlist connectivity, ERC/DRC, pin-to-datasheet checks, BOM math. These run as code — same input, same result, every time. If a check can be written as a rule, it is never left to a model.
Footprint variants, symbol style, layout tradeoffs, end-of-life substitutions. Engineers legitimately disagree here — so agents propose with their reasoning attached, learn your conventions, and leave the call to you.
The harness knows which is which. Every agentic proposal must pass the deterministic checks — ERC, DRC, datasheet match — and land as a reviewable diff. Judgment where it belongs, proof everywhere else.
You express intent in hardware terms. The harness runs the AI underneath.
Say it in hardware terms — "add gigabit Ethernet: PHY, magnetics, and length-matched RGMII." No prompt engineering, no context wrangling. That's the harness's job.
Agents propose before they touch anything. Datasheets, supply data, library rules, and your existing files come in as context — you approve the plan.
Agents place, wire, route, and check in your real files — in parallel, server-side. Close the tab; they keep working while you sleep.
Come back to finished branches. Review visual diffs, roll back anything, merge what's right. Git underneath — you in the loop.
An agent is only as useful as the place it can act. So we built the substrate: a real PCB design tool, in the browser, git-native — where agents read and write the actual files.
Agents read and write real KiCad, Altium, Cadence, and Siemens files. No proprietary intermediate format.
Every agent edit is a commit on a branch. Full traceability, visual diffs, one-click rollback across the whole tree.
Plan-then-edit review, bounded sub-agents, and design-rule checks on every change. Nothing merges without you.
Deploy the harness inside your own infrastructure. Your designs never leave your network.
Getting value from LLMs is its own discipline — context, experiments, orchestration, guardrails — and none of it is electronics engineering. The harness absorbs it.
Bring us the workflow, toolchain, and controls your hardware team needs. We’ll help define where AI belongs and how to connect it safely.