FAQ (21 questions)

An AI-assisted CNC machining-preparation platform that automatically converts 2D engineering drawings into 3D models, G-code, and cutting simulations. AI accelerates drawing recognition, modeling, code generation, and verification, while tolerances, datums, and final pre-machining confirmation stay with professionals (human-in-the-loop). Full explainer: What is AI CNC.

DWG, DXF, PDF, JPG/PNG, STEP, and ZIP are supported (≤25MB per file). Native CAD files such as DWG recognize with the highest accuracy; photographed drawings are affected by shadows, perspective, and overlapping lines — AI may misread through-holes vs. blind holes, so human review is recommended. Details: CAD file formats explained.

Not recommended. Controllers differ in program headers and dialects, and fixturing, tool length, and tool wear on the floor are beyond what a program can cover. The correct flow: 3D cutting simulation → AI dimension cross-check → machinist review and sign-off, then to the machine. Fundamentals: The complete G-code primer.

FANUC is verified on production machines at real sites; Mitsubishi, Siemens, Heidenhain, Syntec, and LNC are adapted through a shop profile (headers / dialects) before deployment. Full details on the specifications page.

3-axis milling (including flip two-sided machining) is verified; 4/5-axis and turning are assessed per site. Materials cover aluminum alloys, carbon/mold steel, stainless steel, titanium and difficult-to-cut materials, copper, and plastics, with cutting parameters generated from your shop parameter library.

Through "AI verifying AI" double-checking: after the model is generated, a second, independent AI cross-compares model dimensions against the 2D drawing annotations one by one, proactively flags anomalies, and a human reviews the result. Method: The complete CNC auto-programming guide.

AI recognizes standard features and standard threaded/round holes; dimensions without individual callouts follow general tolerances. Special tolerances, GD&T, and datum choices involve assembly intent, so a human confirmation checkpoint is kept. See Tolerances and GD&T explained.

Three lines of defense: generation from your shop tool library (fail-safe tool lengths and IDs), pre-machining 3D cutting simulation (overcut / interference / collision), and final machinist sign-off. See Crash prevention in practice.

34 STEP-geometry checks plus 28 G-code checks, item by item (geometric closure, feature dimensions, program syntax, travel and parameter bounds, and more). Every PASS is recorded and traceable, delivered with the machining data package.

No. Start with a controlled pilot: run one real drawing through the full workflow, measure the hours saved, then decide on a daily plan. The three engagement models are on Specs & plans; an assessment framework is in SMB adoption ROI.

Inventory your drawing sources (the mix of DWG/PDF/photos), and file your tool library (IDs, lengths, cutting parameters) plus machine travel and spindle-speed limits. With these boundary conditions, AI output matches your shop reality. Steps: The 90-day adoption timeline.

No. Daily operation is uploading drawings, reviewing results, and signing off; the setup phase is done together with an adoption consultant. The engineer's role shifts to review and correction — see The CNC engineer in the AI era.

No — the opposite: AI takes over repetitive preparation work, while the machinist's tool-selection logic and parameter experience are preserved as a shop knowledge base. See Passing on the master's experience and A day machining with AI.

Drawings are never dropped into public AI services: they stay in the environment you designate, and AI interpretation calls enterprise-grade model services over encrypted channels — under commercial terms with no retention and no training use; de-identification is available first. Fully offline on-premise deployment is on the roadmap, assessed per site. Background: On-premise AI security explained.

Project-level access isolation (each customer sees only their own projects), encrypted transport, and complete operation logs; demo data is isolated under a separate account.

Customer drawings are never used for cross-customer model training. The shop knowledge base (tool library, parameters, proven programs) serves only that shop's own generation workflow.

No conflict — AI takes over the upstream preparation and the machinist finishes in the CAM they know; the two coexist, add value, and integration testing is available. See Mastercam and AI CAM: replace or augment.

ARUMCODE generates code fully automatically from 3D CAD (cloud); CAM Assist embeds in existing CAM to automate ~80% of programming. BestAI CAM starts from where real orders start — the 2D drawing — with confidential-data protection (no retention, no training use; fully offline on-premise on the roadmap) and localized onboarding. Comparison in two case studies: ARUM in Japan, TITANS of CNC field tests.

The free tools (cutting calculators, tolerance lookup, G-code dictionary, and more) solve single-point lookups with no login; the platform solves the whole drawing-to-program workflow. The tools share the same engineering basis as the platform.

Yes — AI-assisted machining workshops (public cohorts / corporate training), covering hands-on AI drawing recognition and modeling, tool-library setup, G-code generation and verification, controlled-pilot design, and ROI measurement. See Training.

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