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Studio AI runs continuously as you design — not as a batch job you trigger at the end. It integrates with your EDA tool and analyses your schematic, PCB layout, power tree, and system architecture in real time. You see findings as you place components and route traces, not after you've submitted for fabrication. The four disciplines — Electrical, Mechanical, Power, and System — are each analysed by a separate AI model trained on design rules, datasheet data, and manufacturing constraints.

EDA tool DRC catches geometric and connectivity violations defined by explicit rules — clearance, trace width, missing connections. Studio AI catches issues that require contextual understanding: a capacitor that passes DRC but is placed too far from its load to be effective, a power rail that sequences correctly but starves a peripheral during startup, an interface that meets voltage spec but violates the protocol timing budget at operating temperature. These are the issues that survive DRC and show up as board failures.

Both. You can import an existing schematic or PCB file for a one-time design audit — useful for inherited designs, designs from acquisitions, or boards going through a second spin. For new designs, Studio AI runs continuously as a background co-pilot. The audit mode produces a prioritised finding report with severity ratings, affected nets or components, and recommended fixes. Most teams run an audit on every existing product before the next revision.

Yes. Studio AI maintains a continuously updated component knowledge base covering millions of parts — operating ranges, derating curves, recommended application circuits, known errata, lifecycle status, and second-source equivalents. When it flags a component issue, it references the specific datasheet parameter it's checking against. You can also upload proprietary component data sheets for custom or ASIC parts that aren't in the public database.

After you upload your BOM, Studio AI cross-references every line item against live distributor pricing, availability data, lifecycle status, and your existing approved vendor list. It then generates an alternate BOM with suggested substitutions — same footprint, same function, lower cost or better availability — ranked by estimated BOM cost saving and risk level. Each suggestion includes the alternate part number, a datasheet comparison, and whether any schematic changes are needed. You approve or reject each suggestion; Studio AI learns your preferences over time.

Single-source components are flagged automatically with a risk score based on manufacturer concentration, lead time history, and geopolitical supply chain factors. For each flagged part, Studio AI identifies qualified second sources — parts that are electrically equivalent and pin-compatible, sourced from a different manufacturer or country of origin. Where a direct drop-in doesn't exist, it flags the design change required to qualify an alternate. This runs at BOM import time and again whenever distributor availability or lifecycle data changes.

Yes — cross-product BOM rationalisation is one of the highest-value features. Upload BOMs from multiple products and Studio AI maps component overlap across your portfolio, identifying opportunities to standardise on a single part number where two products currently use non-identical but functionally equivalent parts. Consolidating to common components unlocks volume pricing breaks, simplifies procurement, and reduces your approved vendor list complexity. This analysis runs across your full portfolio and updates whenever a new BOM is added.

Studio AI integrates with KiCad (open API, deepest integration), Altium Designer and Altium 365, Cadence Allegro and OrCAD, Mentor PADS and Xpedition, Eagle / Autodesk Fusion 360, LTspice and ngspice for simulation file ingestion, and MATLAB/Simulink for system model import. The integration depth varies by tool — KiCad and Altium support live continuous review; other tools support file-based import for batch analysis. The Essentials tier includes KiCad and Altium; all nine integrations are available at Professional and above.

When Studio AI completes a design review, it generates a test plan — a set of acceptance criteria derived from your design specs: expected rail voltages, timing budgets, thermal limits, current thresholds, interface signal levels. Studio Lab ingests this test plan and runs it automatically on the physical board. When the board is flashed and powered, Studio Lab checks each criterion and reports pass/fail against the AI-generated baseline. If a criterion fails, the signal capture is sent back to Studio AI which cross-references it against the original schematic finding to identify root cause. This closes the loop between design intent and physical behavior without manual translation.

The Professional tier runs AI inference in LabForge's cloud — your design files are sent over an encrypted connection, processed, and findings returned. Design data is never stored after the session ends and is never used for model training. The Enterprise tier supports on-premises AI inference for organisations that cannot send design files off-site — the AI models run on your own GPU servers. This is the mode used by customers with export-controlled designs, ITAR requirements, or classified IP.

Studio AI is designed to be a co-pilot, not an oracle. Every finding includes a confidence score, a specific reference (the datasheet parameter, the design rule, the protocol spec) it's checking against, and a clear explanation of why it's flagged. Engineers treat it like a knowledgeable colleague flagging something to check — not an automated verdict. In practice, customers find that high-confidence findings (above 85%) are correct over 90% of the time. Low-confidence findings are surfaced as "worth a look" rather than errors. You can tune the sensitivity threshold per discipline.

Every finding has a one-click "dismiss with reason" action. When you dismiss a finding — either because it's a false positive or because you've made a conscious design decision — Studio AI learns your context. If you dismiss a finding with an explanation (e.g. "this capacitor placement is intentional for thermal reasons"), that note is stored and the same pattern won't be flagged again on this design. Dismissal data is used to improve the model for all customers on an anonymised, opt-in basis.

Yes, in the Enterprise tier. You can upload a custom design rule library — your organisation's internal standards, your CM's process capabilities, your approved vendor list, your derating policies — and Studio AI applies these alongside its built-in rules. Custom rules take precedence over default rules when they conflict. This means Studio AI enforces your company's design standards automatically across every designer on your team, not just the ones who remember to check the style guide.