Neurocad™ is the intent compiler for physical engineering. It turns static engineering artifacts, including datasheets, PDFs, drawings, reference designs, and existing CAD outputs, into native, parametric, design-ready assets that work directly in the tools your teams already use.
Neurocad™ does not replace CAD. It eliminates the manual interpretation, reconstruction, and re-entry required to move engineering information into CAD, preserving design intent while accelerating execution.
No. Conventional conversion tools map formats by converting bytecode without reasoning about intent, thereby losing constraints, relationships, and design-specific context in the process.
The information you need usually already exists. A datasheet contains the pin assignments, package dimensions, tolerances, and thermal limits. The problem is that the information is scattered across pages, leaving an engineer to read the entire document, hold it in their head, and relay it into the next tool by hand. That manual relay is where fidelity and time are lost.
Neurocad™ does the reading in seconds and performs the relay losslessly.
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 do not need to be redrawn, and the assembly behaves the way the original design intended.
The output 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. It does not replace them.
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 CAD licenses.
Most engineering automation tools optimize work inside a specific toolchain, such as better analysis within EDA, faster layout, or cleaner collaboration within a cloud CAD environment. They assume the design is already structured when it arrives.
Neurocad™ addresses the layer before CAD: the conversion of static engineering artifacts, including:
After conversion, they become interactive, native, reusable assets. This is where much of the manual engineering time is lost, and it is the layer established vendors have not addressed.
The clearest differentiator is that 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, learning a new code language, or a change to your existing environment.
Neurocad™ is the parametric design compiler for physical engineering. It is 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 cannot read it.
From the moment an artifact exists, whether it is a datasheet, drawing, reference design, or CAD output, Neurocad™ extracts the intent and delivers native, parametric, design-ready assets directly into the tools that need them. There is 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 also 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 without a native path from evaluation to design.
Neurocad™ addresses specific problems across several 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 do not 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.
Neurocad™ is built by engineers who spent their careers inside the workflows the platform is designed to fix.
The team has experience at Accel EDA, Altium, Autodesk, Meta, Microsoft, HP, and Siemens, building tools used by millions of designers, engineers, and consumers worldwide.
Design intent is the reasoning behind a design decision: the constraints, relationships, and engineering judgment that made a choice valid. It is not the geometry. It is not the netlist. It is what those things mean and why they exist.
Because your tools do not carry it. EDA and CAD tools in your stack are built to hold structured data. They were not built to hold meaning. When a design crosses a tool boundary, such as datasheet to schematic, PCB to enclosure, or simulation to CAD, the geometry moves. The reasoning does not.
What arrives on the other side is a representation. What stays behind is the intent. The engineer on the receiving end then rebuilds it manually. That is intent loss.
At program scale, it compounds into something with a real cost: senior engineers pulled away from forward design, review cycles that run longer than the underlying complexity warrants, and mechanical models rebuilt from scratch because a STEP file carries shape but not constraints.
Neurocad™ captures design intent at the source from datasheets, PDFs, images, reference designs, and existing CAD artifacts. It then propagates that intent as native, parametric, DRC-valid assets directly into Altium, SolidWorks, and other tools downstream.
There is no intermediate format and no manual reconstruction at the tool boundary.
A datasheet arrives as a verified, IPC-compliant schematic symbol, footprint, and 3D model. It is already in your tool and already parametric.
A mechanical assembly survives an electrical update with its constraints intact. The decisions made upstream stay in the workflow.
That is zero re-entry: design intent captured once and propagated everywhere.
Read more: The cost of losing design intent
This is the core insight behind Neurocad™.
A datasheet already contains the design intent required to create the asset: pin assignments, package geometry, tolerances, and electrical and thermal limits.
None of it is necessarily missing. It is simply scattered across pages and formatted for a human to read. Until now, the only thing capable of assembling that information and carrying it into the next tool has been an engineer working by hand, page by page.
Neurocad™ reads the source the way an engineer would and relays a lossless version into the EDA and CAD tools downstream.
The information was always there. Neurocad™ moves it without fidelity loss.
The reconciliation tax is what engineering organizations pay when design intent must be manually reconstructed at every tool boundary across a program. It includes symbols and footprints recreated from datasheets that already contained the information, parametric constraints rebuilt after a STEP handoff, and BOMs reconciled by hand against a design that has moved on without them.
Individually, each instance looks like small overhead. Across multiple engineers, multiple tool boundaries, and multiple revision cycles on a single program, the tax compounds.
It appears as review cycles that run longer than they should, senior engineers pulled away from forward design to re-derive constraints already captured upstream, and NPI schedules that slip because moving data between tools requires human mediation at every boundary. Intent loss is the root cause. The reconciliation tax is the accumulated cost.
Zero re-entry eliminates it. Design intent is captured once and propagated into every target environment without manual reconstruction.
Read more: The cost of losing design intent
No.
Most tools in this category generate a footprint or symbol and hand it to the engineer. That is where their value stops.
The underlying data problem, the gap between what the datasheet specifies and what CAD needs to build, is never resolved. It simply moves one step downstream, from the engineer to a tool that produces geometry without exposing what it assumed along the way.
Neurocad™ works differently because the output is not the starting point. Before any geometry is generated, the system extracts and interprets the dimensions, constraints, and relationships in the source material. It then surfaces that interpretation for the engineer to review.
The distinction is that the reasoning behind the asset is visible, correctable, and repeatable. It is not delivered as a single output the engineer is asked to trust without seeing how it was derived.
The manual process in electronics engineering workflows, including extracting data from datasheets, rebuilding parametric constraints after ECAD-to-MCAD handoffs, and 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 by extracting design intent from source documentation and delivering native, parametric assets directly into tools such as Altium, Cadence, and SolidWorks without manual reconstruction at the tool boundary.
That is zero re-entry. It is not simply a feature inside an existing tool. It is the infrastructure layer that removes the manual step.
Read more: What works inside systems engineering tools, what fails between them, and what fills the gap
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. It applies proportional reasoning and manufacturing tolerance standards before geometry is generated. This allows the system to produce accurate, manufacturable geometry 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 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 rather than 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, including 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.
AI and deterministic generation perform different jobs inside Neurocad™.
AI is useful for interpreting irregular source material. Datasheets, drawings, tables, and reference designs are rarely organized in one perfectly consistent format. Understanding them requires context.
Final engineering geometry requires repeatability. Once the extracted intent has been reviewed and confirmed, generation is deterministic. The same confirmed intent produces the same result. The AI helps interpret what the source material means. The kernel executes what has been confirmed. That separation is central to the system.
Probabilistic interpretation belongs at the front of the workflow. Production geometry should not depend on improvisation.
A generated asset is useful. A generated asset that can be checked against its original engineering requirements is far more valuable.
For supported workflows, Neurocad™’s verification pipeline can run the generated model and compare its behavior with performance information contained in the original source material.
That creates a connected process:
Source documentation becomes extracted intent. Confirmed intent becomes deterministic generation. Generated assets proceed into verification.
This connected process is the larger issue Neurocad™ is addressing.
Engineering organizations need systems that can carry engineering intent across tool boundaries and turn it into native, inspectable, governable assets.
The bottleneck has always been turning engineering information into something the toolchain can use, not accessing the information itself.
That is the work Neurocad™ is built to remove.
Neurocad™ is designed to work with real-world engineering artifacts, not idealized inputs.
Supported input categories include:
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.
There is no intermediate format, post-processing, or manual cleanup.
Neurocad™ supports 3D modeling and 2D design.
It generates parametric 3D geometry from datasheets, drawings, and specifications. It also supports 2D symbol and footprint creation for ECAD workflows.
Simulation-ready assets, including geometry, constraints, and properties, are on the roadmap and are built on the same kernel capabilities available in the platform today.
Neurocad™ handles complex geometry, including:
No.
Your PLM, PDM, library-management system, version-control environment, and approval process continue doing the jobs they were selected to do.
Neurocad™ operates earlier in the workflow.
It creates the native engineering asset that those systems will eventually manage, govern, release, and track. That separation is deliberate.
Engineering teams have already invested years in building processes around their existing systems. The last thing most organizations need is another platform migration disguised as automation.
Neurocad™ is not asking teams to change how they manage engineering data. It removes the manual reconstruction required before that data is ready to be managed.
Generating an asset is the easier half of the problem.
The harder half is making sure the asset behaves like every other part already in your library. That includes maintaining the same relationships between the symbol, footprint, and 3D model, the same package logic, and the same structure your existing tools expect a part to follow.
Neurocad™ treats a symbol, footprint, and 3D model as one part, not three unrelated files.
IPC-based footprints are generated with standard density variants rather than a single arbitrary geometry. Those variants share a package relationship so they behave consistently inside the library structures ECAD tools already use, such as Altium’s integrated library model.
For teams standardizing at scale, the same generation logic can run through Git-triggered pipelines. A library can be built or brought into alignment across many components and repositories rather than one component at a time.
Naming conventions, custom layer-stack mapping, and organization-specific governance rules vary by team.
Neurocad™ is built to work within the conventions your organization already uses rather than imposing new ones. Where a specific convention must be configured, that should be addressed directly during implementation.
For PCB footprint generation, Neurocad™ works from IPC-7351 package specifications.
Instead of treating the datasheet drawing as a picture to be copied, the system identifies the applicable package specification and generates the footprint from the engineering rules associated with that package.
That matters because a footprint is not simply a collection of pads and lines.
It contains decisions about copper, solder mask, paste, spacing, tolerances, courtyards, and assembly clearance.
Those decisions should be based on repeatable engineering standards rather than reconstructed by hand each time a component enters the workflow.
Pin mapping is established before geometry is generated.
Neurocad™ extracts pin information from the source material and presents that interpretation for the engineer to review. Pin 1 orientation, numbering, and relationships are visible before they become part of the finished asset. This is where confidence in engineering automation should come from.
The system should not simply produce an answer and ask the engineer to trust it. It should expose what it understood, allow that understanding to be confirmed, and only then execute.
Engineering judgment remains part of the process, but it moves to a more useful point in the workflow. In a traditional process, an engineer often reviews the completed asset after the reconstruction work has already happened.
Finding a problem at that stage means tracing the error backward through geometry, documentation, assumptions, and manual decisions.
Neurocad™ moves that review upstream.
Before generation, the engineer can see the extracted dimensions, parameters, pin mapping, constraints, and relationships. Nothing is generated until that intent is confirmed. The purpose is not to remove the engineer from the process. It is to stop using engineering judgment for repetitive transcription and reconstruction.
Review the intent once. Then let the system execute it consistently.
Complex parts are common, not rare exceptions.
Neurocad™’s asset model accounts for hierarchical schematics, sub-parts, and multi-part components because a part is rarely just one file.
A single package can involve multiple sub-symbols mapped across different schematic sheets, IPC footprint variants tied to a shared package relationship, and pin mapping that must remain consistent across the entire structure. None of that removes the value of the intent-review step. It is where edge cases matter most.
Before anything is generated, the engineer can see how Neurocad™ interpreted a hierarchical symbol, multi-part component, or nonstandard footprint.
They can correct anything that looks wrong and confirm it before geometry is produced.
Advanced layer stacks, keepouts, and manufacturing-specific layers follow the same pattern. They are reviewed as extracted intent before becoming part of a committed asset.
Complexity does not bypass the review step. It is the reason the review step exists.
Outputs are written directly into the native format of the target CAD application through the local desktop service.
Parameters, constraints, and feature history travel inside those native containers.
Neutral formats are supported for import, where they serve as one of many possible input sources.
DXF is treated as an input format, not an output format. Neurocad™ can ingest DXF files as source material for intent extraction. DXF does not carry parametric history. It encodes 2D geometry as flat drawing entities.
When a DXF arrives as input, Neurocad™ extracts dimensional data from it and uses that information as the basis for native synthesis.
The output goes to the target CAD application in that tool’s native format, not back to DXF.
They do not carry them. STEP, or ISO 10303, is a boundary-representation format for geometric solids. It records the final shape of a body as faces, edges, and vertices with associated surface equations.
It does not record the construction history that produced the shape. There are no sketches, feature sequences, constraint relationships, or design intent.
A part exported from SolidWorks to STEP and re-imported arrives as a static solid. The engineer can measure it but cannot change its diameter by editing the original parameter.
This is the fundamental reason Neurocad™ writes native assets into the target tool rather than relying on neutral interchange formats.
Read more: Why the mechanical model breaks after a board revision in PCB enclosure design
Feature history depends on a named, ordered construction sequence, such as sketch, extrude, fillet, and mate, that is tool-specific and kernel-specific.
In Fusion 360, geometry is computed from equations stored as boundary representations. Sketch entities do not have stable names at runtime, only handles allocated dynamically. This makes querying or modifying a feature by name after the fact unreliable.
In SolidWorks, VBA-driven scripts that set feature parameters can produce syntax errors that prevent the model from loading, even when the geometry preview appears correct.
Neurocad™’s architecture addresses this with a script generator that produces a single parameterized construction script and compiles it into tool-specific output, such as:
The goal is a result that loads and remains editable, not one that is merely visually correct.
What travels across the network is the engineering intent required to create the asset, not the completed asset itself.
When Neurocad™ reads a datasheet, drawing, image, or reference design, it identifies the dimensions, parameters, constraints, pin relationships, and other information that define what needs to be built.
That structured description is what gets sent. The finished symbol, footprint, or 3D model is generated locally through the Neurocad™ desktop service.
The completed CAD asset comes into existence on your machine, where it can move directly into the engineering tools your team already uses.
This is an important architectural distinction. Neurocad™ does not need to possess the finished design to help create it.
The safest system is one designed to avoid collecting data it does not need.
Neurocad™ works from the source information and engineering intent required to generate an asset. It does not require your finished proprietary design or existing CAD library to be uploaded before it can perform the work.
When a datasheet, drawing, or reference design is staged for processing, it is encrypted end to end from the moment it enters the browser. What gets extracted and sent onward is the engineering intent, not the source file itself.
Schematics, PCB data, and library content never travel over the wire.
The generated asset remains on your machine. Data at rest on your machine is encrypted, and Neurocad™ does not have access to it. Your existing library remains where it is.
Your established controls over design data remain in place, and your PLM, PDM, and version-control systems continue operating as they did before Neurocad™ entered the workflow. Security should not depend entirely on policy language. It should be visible in the architecture of the product.
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. Neurocad™ is designed to fit into existing engineering environments, not force a new infrastructure model onto your organization.
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 synchronized with documentation updates.
They solve different parts of the same workflow.
SnapMagic is a curated CAD library: a large, well-maintained collection of schematic symbols, footprints, and 3D models that engineers can search to find an existing part. It is useful when the component you need is already in the catalog.
Neurocad™ starts where the catalog ends. Instead of looking up a model that already exists, Neurocad™ generates one from the source document.
Give it a datasheet, package drawing, or specification, and it produces a native, parametric, DRC-valid symbol, footprint, and 3D model directly in your tool.
This includes custom components, newly released parts, and anything else you would otherwise build from a datasheet by hand. You review every extracted dimension before anything is committed. When the part you need is not in the library, that is the point Neurocad™ was built to address.
SamacSys, also known as the Component Search Engine, is a curated library of free, verified symbols, footprints, and 3D models. When a part is not in the library, the SamacSys team can build it on request. That is a human build with an associated turnaround time.
Neurocad™ generates the asset from the datasheet in front of you, when you need it. It works with existing, new, custom, and uncatalogued parts without waiting for a manual build.
See “How is Neurocad™ different from component libraries?” for a broader comparison.
Ultra Librarian is a PCB CAD library with more than 16 million verified parts available in many formats.
Like any library, its coverage is limited by the size of its catalog. Its 3D models are commonly delivered as STEP files, which carry shape but not the parametric history that makes a model editable.
Neurocad™ generates from the datasheet rather than searching a catalog. It produces true parametric models for tools such as SolidWorks, Fusion 360, and Inventor rather than relying solely on static STEP geometry.
Neurocad™ is also a vendor-agnostic tool funded by the engineers who use it, with no stake in which component they select or which design tool they use.
See “How is Neurocad™ different from component libraries?” for a broader comparison.
Component libraries are lookup tools.
You search a catalog of pre-built symbols, footprints, and 3D models and download the one you need. They are widely used and genuinely useful when the part already exists in the catalog.
Neurocad™ is synthesis, not lookup. It does not maintain a catalog. It reads the source document, whether that is a datasheet, drawing, or specification, resolves ambiguous or missing dimensions through ratiometric inference, and generates the asset on request through native synthesis.
Coverage
A library can only serve what has already been built. Its catalog is its ceiling. When a part is not there, the options are to wait for it to be built or build it yourself by hand. Neurocad™ generates from the source documentation, allowing it to cover custom parts, newly released parts, and the long tail no library is likely to carry.
Scope
Most component libraries primarily serve the electronic side of the workflow. Their 3D models are commonly delivered as STEP files, which carry geometry but not the construction history that makes a model editable. Neurocad™ generates true parametric models for SolidWorks, Fusion 360, and Inventor so design intent survives the ECAD-to-MCAD handoff rather than arriving as a static solid.
Posture
Neurocad™ is a vendor-agnostic parametric design compiler funded by the engineers who use it rather than by component sales or a CAD platform. It has no stake in which component you select or which tool you use. The asset only has to be correct.
The two approaches can work together.
A library is the fastest path to a part that already exists. Neurocad™ is the path to the part that does not, and the way to keep design intent intact across every tool boundary downstream.
That is zero re-entry.
Read more: What works inside system engineering tools, what fails between them, and what fills the gap