AI Engineering Platform · Enterprise pilot
“The AI said so” is not an engineering record.
Your engineers are already asking AI about parts. The question is whether the answer can be traced to a source, reproduced by a calculation, and signed so it survives a design review, a PPAP audit or a deposition. The ETS platform is the governed layer that makes that true. It already runs underneath ChatETS and ETS Build.
The problem
Generic AI is confident. Engineering needs accountable.
A chatbot that sounds right is dangerous in a discipline where a wrong wall thickness costs $25K–$75K after steel is cut. General-purpose models have no production memory, no view of your specs, no calculator they are obliged to use, and no record of what they told whom.
Engineering organizations don’t need a smarter guess. They need an answer with provenance: which document, which calculation, which version, signed by which system, on which date.
| Question | Generic AI assistant | ETS platform |
|---|---|---|
| Where did this come from? | Training data it can’t cite | Your documents and the ETS corpus, cited to document and page |
| Is the number right? | Generated text that looks like math | Routed to a verified calculation module and returned with inputs |
| Who else can see it? | Shared vendor cloud | Organization-scoped isolation; self-hosted or private cloud |
| Can we prove it later? | Chat history, if you saved it | Cryptographically signed analysis plus an immutable compliance ledger |
| Does it get better? | When the vendor retrains | Failure memory: mistakes are recorded and fed back into your deployment |
The loop
Ask. Ground. Compute. Sign.
Every request moves through the same four stages. Each stage leaves an artifact you can inspect.
Orchestrate
The request is routed to specialized engineering agents for materials, design, manufacturing and analysis, in sequence or in parallel, with session state and role-based routing.
Ground
Agents retrieve from your ingested standards, specs, test reports and drawings (PDF, DOCX, PPTX) through hybrid semantic and keyword search, alongside the ETS corpus.
Compute
Numbers come from tools, not prose: physics, FEA, calculators and manufacturing checks invoked through a governed, schema-validated tool layer with per-organization rate management.
Sign & learn
The analysis is cryptographically signed and written to a compliance ledger. Failures are captured in failure memory and used to improve prompts and tools, scored against quality rubrics.
Capabilities
Six layers, one audit trail.
The same platform that powers ChatETS and ETS Build, exposed for your own workflows.
Multi-agent orchestration
- Sequential & parallel workflows
- Role-based task routing
- Service discovery & health
Knowledge grounding
- PDF, DOCX, PPTX ingestion
- Semantic + keyword search
- Citations with sources
Tool execution
- FEA, physics, calculators
- Schema-validated calls
- Job queuing per org
Self-improvement
- Failure memory & adaptation
- Prompt and tool optimization
- Quality evaluation rubrics
Research exploration
- Agent-population experiments
- Knowledge sharing
- Emergent-behavior analysis
Verification & compliance
- Signed analyses
- Compliance ledger
- Full audit trail
An AI answer becomes an engineering record the moment you can prove where it came from.
Who it’s for
Built for the people who sign off.
Four stakeholders, four reasons to care.
Workflows that document themselves
Provenance is produced by the workflow, not reconstructed afterwards for the audit.
Your walls, your keys
Organization-scoped isolation, gateway authentication, SSO and full self-hosting.
Evidence that survives an audit
Signed analyses and an immutable ledger for IATF 16949, ISO 13485 and FDA-regulated programs.
Defensible decisions
When a design decision is challenged, the record shows the source, the math and the timestamp.
The pilot
A small number of organizations. Scoped honestly.
The platform is in enterprise pilot. We run a limited number of pilots with engineering organizations that have a real workflow to govern: a design-review process, a supplier-quality loop, a compliance package.
A pilot starts with a scoping call, runs against your own documents inside your environment, and ends with a written assessment of what worked, what didn’t and what production deployment would take.
- Scope before commitment. We define the workflow, success criteria and data boundaries first.
- Your environment. Self-hosted or private cloud; your data never trains anyone else’s model.
- Senior engineers attached. The people who built the ETS corpus review the outputs.
- Start free today. ChatETS runs on the same platform and costs nothing to try.
Next step
Govern the AI your engineers are already using.
Tell us the workflow you need to trust. A senior engineer replies within one business day to scope a pilot.
- NDA on request
- Senior engineer replies in one business day
- Fixed price where applicable
- Your data stays yours