Give your engineers back the hours the paperwork takes
Engineering AI automation applies language and vision models to the document-heavy side of engineering work: reading customer drawings and specs, extracting BOMs, checking requirements coverage, assembling quotes, and generating test documentation. It removes the pattern where degreed engineers spend a third of their week transcribing and cross-referencing instead of engineering.
Willowark approaches this as engineers, which shapes the architecture. Anything deterministic — parsing STEP or DXF geometry, checking numbers against tolerance rules — is done in code, where it is exact. Models handle the interpretive work: reading notes on a drawing, mapping a customer spec to your capabilities, drafting a procedure. An engineer reviews before anything binds the business.
AI & Intelligent AutomationHow the work gets done
The same way every time: scope, build, hand over.
A quoting pipeline, for example, ingests the customer's drawing package, extracts geometry, materials, tolerances, and finish callouts, checks them against your process capabilities, and assembles a draft quote with its assumptions listed — flagging any callout it could not confidently read. A compliance pipeline maps requirement lines in a spec to evidence in your documentation and shows the gaps. In both, model confidence is surfaced, never hidden.
Integration targets the systems engineering already lives in: PLM, ERP, PDM vaults, requirements tools. Success is measured in turnaround and coverage — quotes out in hours instead of a week, every requirement line traced — and in engineer-hours moved from transcription back to design work.
Scoping begins with a stack of real drawing packages or spec revisions, chosen to include the awkward ones. We work through a handful with your engineers to learn where the time actually goes, which is often not the reading but the cross-referencing against internal capability tables and past jobs. The main trade-off is how much to automate before the engineer sees it. Extracting and flagging is low risk; assembling a full draft quote with pricing logic is higher risk and needs your costing rules made explicit, which is sometimes the hardest part of the project. We usually build extraction first and add assembly once the extraction is trusted.
What goes wrong in practice is drift in the inputs: a customer switches CAD systems and the exported drawings lose their layer conventions, a spec template changes its numbering, or a new material shows up that the capability table does not cover. We design against that by validating each package against expectations on intake and routing anything unfamiliar to an engineer before extraction runs, and by keeping the capability and costing rules in files your engineers can edit without a developer. At handover you get the pipelines, the rule files, and the correction history, which doubles as a record of how your quoting assumptions have changed over time.
Scope it in writing
What we agree before work starts
- Automated pipelines for drawing intake, extraction, and capability checks
- Draft quote or compliance packages with stated assumptions and flagged uncertainties
Build with checkpoints
Working results, not slide decks
- Requirements traceability tooling mapping spec lines to evidence
- Integrations with your PLM, ERP, and document vaults
Hand over something you own
Documentation, source, and training
- Engineer review workflow with correction capture that improves the system
- Editable capability and costing rule files with documentation for engineers to maintain them
Sound familiar?
Where engineering ai automation earns its keep.
A job shop where quoting a drawing package takes a senior engineer half a day
An OEM checking every customer spec revision against hundreds of internal documents
A quality team writing inspection plans by re-reading the same drawings each time
A test lab turning raw results into formatted reports by hand
Ask about Engineering AI Automation
Describe the problem. Get a straight answer.
One line is enough. An engineer replies within a business day.
Related work
AI operating software for training operations
Components:
- Schedulers (training ops): The people running the training operation.
- Operating software (AI assistance): The operating software, with AI assistance built in.
- Cloud services (distributed): Distributed cloud services behind the application.
- Training game (learning): The training game the software connects to.
Connections:
- Schedulers to Operating software
- Operating software to Cloud services
- Operating software to Training game
A global automotive manufacturer's training operation · Automotive
AI operating software for training operations
AI operating software for the manufacturer's training schedulers — removing manual scheduling labor and logistics tracking, saving hundreds of hours per year — plus a training video game built to help trainers perform better. Used across the manufacturer's training organization.
Read the case study →Common questions
Asked before every engineering ai automation project.
Can AI actually read engineering drawings?
Increasingly well, with honest limits. Title blocks, BOM tables, notes, and standard callouts extract reliably; dense GD&T on a crowded sheet is harder, and the system is built to know the difference. Where native CAD files exist, we parse geometry directly rather than reading a rendering, which is exact.
Who is responsible if the AI misreads something?
Your engineer signs, and the system is built around that fact. Output is a draft with assumptions and low-confidence items explicitly flagged, so the engineer checks the flagged items instead of re-deriving everything. That is where the time savings come from without moving liability onto a model.
Will this work with our PLM and ERP?
That integration is most of the point — a quote drafted outside your systems just creates new re-keying. We build against the APIs of your PLM, ERP, and PDM vault, and where a legacy system lacks an API we work through its database or file interfaces. Integration scoping is part of discovery.
How do you handle customer drawings that are confidential or under NDA?
The same way your engineers do, with the controls written into the system. Drawing packages stay in your environment or your cloud tenancy, model calls run under commercial terms that exclude training on your data, and where a customer's terms are stricter, we route their packages to a model you host or exclude them from the automated path entirely. Access to the pipeline follows the same permissions as your PDM vault.
Our quoting depends on tribal knowledge. Can that be captured?
Partly, and the attempt is usually valuable on its own. Building the capability and costing rules forces the unwritten assumptions into a file that can be reviewed and argued over, which often surfaces disagreements between estimators that nobody knew existed. What does not capture cleanly stays with the engineer, and the system is built so their corrections are recorded. Over time the corrections show which rules are missing and which judgment calls are genuinely judgment.
Where this sits
Engineering AI Automation, inside a ai & intelligent automation system.
The lit component is the part of the system this service delivers; the rest is what it has to work with.
Hover or focus a component to see what it is and what it talks to. Arrow keys move between them.
Inbound documents and messages are ingested, an agent reasons with company knowledge and acts through the systems of record, and a person reviews the cases that need judgment.
Components:
- Inbound (email, PDFs, forms): The unstructured work arriving every day.
- Ingestion (extract, classify): Turns documents into structured fields with confidence scores.
- Agent (reasons, uses tools): A model with tools: it looks things up, decides, and acts — within limits you set.
- Knowledge (your docs): Company procedures and history, retrieved on demand.
- Systems of record (ERP, CRM): Where the work actually lands.
- Reviewer (exceptions): The person who sees what the agent was unsure about.
Connections:
- Inbound to Ingestion over email
- Ingestion to Agent over events
- Agent to Knowledge over REST, both directions
- Agent to Systems of record over REST
- Agent to Reviewer over handoff
Strategy. Software. Systems.
Have a system that should exist?
Tell us what your operation is doing manually, what isn't connected, or what you're trying to build. We'll tell you plainly whether and how we can help.