Neurocad is the intent synthesizer for physical engineering. It turns static design artifacts — datasheets, PDFs, drawings, reference designs, and CAD outputs — into native, parametric, design-ready assets that work directly in the tools your teams already use.
It is not a CAD replacement. It is the intent layer that sits between unstructured engineering documentation and native CAD execution, eliminating the manual translation work that consumes a disproportionate share of engineering time.
No. Conventional conversion tools map formats converting bytecode, without reasoning about ‘intent’, thereby losing constraints, relationships, and design-specific context in the process.
Neurocad performs native synthesis. It extracts design intent from source documentation — resolving ambiguous or incomplete dimensions using ratiometric inference and manufacturing tolerances — then generates fully structured, parametric assets directly inside target tools. Parameters, constraints, and relationships are preserved, not approximated. This means the geometry holds under revision, the footprints don't need to be redrawn, and the assembly behaves the way the original design intended.
The output is not a translated file. It is a design-ready asset that participates in validation, layout, and simulation workflows from the start.
No. Neurocad integrates with the tools your engineers are already proficient in: Altium, Cadence, SolidWorks, Fusion 360, CATIA, Inventor, Siemens, OnShape, and others.
Your CAD environment stays intact. Neurocad is the intent layer between engineering content and those tools. It removes the manual reconstruction work that precedes CAD authoring, enabling clean, native outputs without requiring tool migration or additional licenses.
Neurocad is designed to work with real-world engineering artifacts, not idealized inputs. It accepts:
If it exists in your engineering documentation environment, Neurocad is built to ingest it.
Neurocad generates native, production-ready design assets including:
Outputs are written directly into target ECAD and MCAD environments. No intermediate format, no post-processing, no manual cleanup.
Yes to 3D modeling and 2D design. Neurocad generates parametric 3D geometry from datasheets, drawings, and specifications, and supports 2D symbol and footprint creation for ECAD workflows. Simulation-ready assets — including geometry, constraints, and properties — are on the roadmap, built on the same kernel capabilities available in Early Access today.
It handles complex geometry: B-Spline, Cubic Curve, Bezier, NURBS Curves, and NURBS Surfaces are all supported.
Neurocad is infrastructure-flexible. Collaboration, version control, and sharing are handled in the cloud. Data and assets can be accessed and opened in local tooling where required by your workflow, compliance requirements, or data governance policies.
It is designed to fit into existing engineering environments. Not to force a new infrastructure model onto your organization.
This is a core capability, not an edge case. Most real-world engineering documentation is incomplete.
Neurocad uses ratiometric inference to resolve ambiguous or missing dimensions — applying proportional reasoning and manufacturing tolerance standards before geometry is generated. This means the system produces accurate, manufacturable geometry even from imperfect source documents, rather than failing or propagating errors downstream.
Neurocad uses a combination of machine learning, reinforcement learning, and deterministic geometry generation. It is not a general-purpose language model applied to CAD.
The system is purpose-built for engineering workflows by a team previously at Accel EDA, Altium, Autodesk, Meta, Microsoft, HP, and Siemens — building tools used by millions of designers worldwide. The AI component handles intent extraction and inference from unstructured sources. The parametric modeling kernel then converts that structured intent into manufacturable geometry using deterministic, constraint-driven generation, not probabilistic output.
The result is consistent, verifiable, and revision-safe.
Before any asset is generated, Neurocad surfaces a structured model of how it interpreted the source material. Engineers can inspect this intent model — reviewing the parameters, dimensions, constraints, and relationships the system extracted — and refine it before generation begins.
This keeps design intent transparent and auditable. It also means the intent model becomes a reusable foundation for downstream tools, teams, and derivative designs.
Yes. Neurocad supports git-triggered automation pipelines that scale from single-component validation to full library generation across teams and repositories. Pipelines can be configured to run on commit events, enabling continuous generation workflows that keep design assets in sync with documentation updates.
Neurocad is currently in an Early Access Program, working with a select group of engineers on documentation-driven component generation workflows — specifically symbol generation, footprint generation, and 3D model creation for ECAD and MCAD environments.
Applications are reviewed manually. Early Access is not intended for production use.
Neurocad addresses specific problems across three types of engineering organizations:
Electrical and electronics design engineers who spend time extracting data from datasheets, recreating footprints and symbols, and cleaning up broken ECAD imports.
Mechanical and CAD engineers who deal with broken ECAD-to-MCAD handoffs, non-parametric imported geometry, and models that don't hold up under revision.
OEM engineering organizations operating across multiple CAD tools, geographies, and programs — where design intent is routinely lost during handoffs and reuse requires rebuilding from PDFs.
Semiconductor and component suppliers whose reference designs and application content are published as static files, losing attribution, design intent, and visibility once a customer downloads them.
Electronic component distributors whose product pages send engineers off-site during the highest-intent moment of evaluation.
Most engineering automation tools optimize work inside a specific toolchain — better analysis within EDA, faster layout, cleaner collaboration within a cloud CAD environment. They assume the design is already structured when it arrives.
Neurocad addresses the layer before that: the conversion of static engineering artifacts — PDFs, datasheets, drawings, archived designs — into interactive, native, reusable assets. This is where most engineering time is actually lost, and it is the layer that established vendors have not addressed.
The clearest differentiator: Neurocad begins with real-world, unstructured engineering documents and produces native outputs for enterprise-grade tools. It does not require structured inputs, a specific tool, or a change to your existing environment.
Neurocad is the intent synthesizer for physical engineering — the layer that sits between unstructured engineering artifacts and the tools your teams already use.
The problem is the same at every stage: the information exists, but the tools can't read it. From the moment an artifact exists — a datasheet, a drawing, a reference design, a CAD output — Neurocad extracts the intent, and delivers native, parametric, design-ready assets directly into the tools that need them. No manual re-entry at any boundary.
That spans electrical and mechanical engineering workflows, systems engineers managing complexity across both, and OEM organizations where intent is routinely lost between programs and teams. It extends to semiconductor and component suppliers whose reference designs leave their hands as static PDFs with no visibility into what gets used, and distributors where engineers make high-intent component decisions with no native path from evaluation to design.
Neurocad is built by engineers who spent their careers inside the workflows this platform is designed to fix. Previously at Accel EDA, Altium, Autodesk, Meta, Microsoft, HP, and Siemens, building tools used by millions of designers, engineers, and consumers worldwide.
The manual process in electronics engineering workflows — extracting data from datasheets, rebuilding parametric constraints after ECAD-to-MCAD handoffs, reconciling BOMs by hand — is caused by a structural gap between unstructured engineering documentation and the CAD tools that need structured data. Most EDA tools operate inside that gap rather than closing it. Neurocad is built specifically to close it: extracting design intent from source documentation and delivering native, parametric assets directly into tools like Altium, Cadence, and SolidWorks without manual reconstruction at any boundary. That is zero re-entry. Not a feature inside an existing tool, but the infrastructure layer that removes the manual step entirely.