01
Production-line quality triage
An agent reads sensor and inspection data, checks it against the spec sheet, and drafts a defect report for a line supervisor to confirm — before a bad batch reaches the next station.
Agentic AI in automotive
Agents that query production data directly.
We build agents that query production, claims, and supplier data directly — turning a plant manager's or claims adjuster's question into a validated database query, not a chatbot layered on top. Every query and action is logged, so quality and engineering teams can trace exactly what the agent looked at and why.
Trusted by operators
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+11.76% orders from day 1 of the Australian launchThree-agent product advisor guiding customers through print-order configuration.
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Supply chain intelligence agents reducing manual coordination overhead.
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37 kernels ported from CUDA to Intel GaudiProduction AI workloads moved to new hardware for on-prem LLM deployment.
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AI agent implementation for a global automotive enterprise.
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2 hrs → 3 min to generate a workflowText-to-workflow agents building validated node graphs inside the platform.
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0 hallucinations across 329+ validation scenariosHIPAA-compliant agentic RAG over proprietary clinical triage guidelines.
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Why agentic AI
Automotive engineering runs on production data most AI never touches
Manufacturing execution systems, quality telemetry, warranty claims and supplier records are highly structured, and scattered across MES, ERP and dealer systems that do not talk to each other. A chatbot layered over that will answer confidently and cite nothing. A plant floor, where a wrong call has real cost, needs an agent that queries the underlying data directly, cross-references specs and bulletins, and escalates anything it is not confident about.
every query traced back to source data
Use cases
Where agents earn trust in automotive operations
Workflows where production and claims data already live in structured systems — the agent queries them directly instead of guessing.
02
Warranty claims verification
Cross-checks a claim against the repair order, vehicle history, and manufacturer service bulletins before it reaches an adjuster — catching mismatches a single-system lookup would miss.
03
Supply-chain exception handling
Monitors supplier and inventory data for part shortages and drafts a reroute or substitution recommendation for a procurement lead to approve, instead of a line stoppage nobody saw coming.
04
Technician diagnostic assist
Retrieves the service bulletins and wiring diagrams tied to a diagnostic trouble code and drafts a repair path for the technician to verify, replacing a manual lookup across multiple manuals.
Evidence from production
Engineering-workflow orchestration, measured in production
None of these numbers come from an automotive deployment — we have not shipped a production agent inside an OEM or a tier-one supplier yet. Synera is the closest adjacent match: engineering software, where the agent automates multi-step engineering work of the same class as an engineering-change or validation workflow. Mixam measures multi-tool orchestration at scale, and Schmitt-Thompson measures validation before a safety-relevant output. Every figure links to the case study behind it.
Agents do not release an engineering change, approve a warranty payout, or act on the line. They query, cross-check and draft what sits before an engineer's sign-off. Every step is logged for review.
Manual setup per multi-step workflow
2 hrs
Not an automotive deployment — engineers on Synera's platform assembled each complex workflow by hand, node by node.
95.4%
Success rate in workflow results
A three-agent product advisor guiding customers through print-order configuration — 15 tools working against more than a billion product combinations.
0 hallucinations
across 329+ nurse-validated scenarios
Nurse-triage guidance where a wrong recommendation is a patient-safety event — staged retrieval and validation rather than one model answering in a single pass.
Client results
Proof from production agentic deployments
We have not shipped a production agent inside an automotive manufacturer or supplier. Synera is the closest match — engineering software, automating the same class of multi-step engineering work as change and validation workflows on a vehicle program. Mixam shows the multi-tool orchestration and Schmitt-Thompson the validation gate that work depends on.
Delivery path
From workflow audit to a production agent on your plant floor
TriStorm keeps engineering, quality, and IT aligned, so the agent works within existing MES and ERP boundaries, not around them.
Map workflow and data access
We audit the target workflow (production, claims, or supplier data) and the systems of record it touches, then rank use cases by engineering impact and integration risk.
- Workflow audit
- Data access map
- Prioritized use case
Build and validate the agent
We build against your real production data shapes (schemas, sensor formats, claims fields) and validate every output against an evaluation suite before it reaches a reviewer.
- Working prototype
- Evaluation suite
- Escalation rules
Deploy with monitoring
Production rollout with logging on every query and action, plus a structured handoff so your engineering and quality teams run the system independently.
- Production deployment
- Audit trail & monitoring
- Operator runbook
Not sure where to start?
A 30-minute call is usually enough to find your highest-value use case
Talk directly to our founders and PhD AI engineers. We will show you real results from 30+ agentic projects and walk through how to apply them to your own engineering-change and supplier workflows. Every example is something already running in production.
Independence
How we help you stay independent
Your team owns what we build. We work on open-source foundations, and the agent logic, the integrations and the evaluation harness transfer to you at the end of the engagement.
Technological sovereignty
We have delivered systems that run with no connection to a big-tech platform: sovereign AI, engineered in Europe.
Small language models
Smaller models keep token costs predictable in day-to-day operations and let the system run on your own internal or on-premise infrastructure.
Open source
We build on open-source software as contributors and as an official Pydantic implementation partner, so the stack stays inspectable and your team keeps the source.
FAQ
Agentic AI in automotive, answered
Where does agentic AI actually fit into automotive operations?
In workflows where production, claims, or supplier data already lives in a structured system of record — quality triage, warranty verification, supply exception handling, diagnostic lookup. We do not deploy agents to make unsupervised decisions on the line; we deploy them to query, cross-check, and draft, with an engineer or reviewer as the final gate.
How is this different from the automation already running on our production line?
Rule-based automation reacts to a fixed condition. An agent reasons across multiple data sources (sensor readings, spec sheets, historical claims) and adapts its query path to what it finds, the same reasoning-plus-validation pattern behind Synera's agent platform, which reads engineering intent and generates a fully validated workflow through multi-step checks rather than a single pass.
What automotive workflows do agents typically handle first?
Quality triage on inspection data, warranty claims cross-checked against service bulletins, supply-chain exception flagging, and diagnostic assist for technicians. We start with whichever workflow has the clearest data access and the most manual hours behind it.
How do you handle data governance across MES, ERP, and dealer systems?
Agents connect through your existing access controls — role-based permissions on what they can query, no write access without a human step, and a log of every query and action taken. We map the data path and governance boundary before we design the agent.
What happens when the agent hits data it is not confident interpreting?
It escalates. Confidence thresholds and schema or data gaps route to a human reviewer with the reasoning and underlying query attached, so nothing acts on an uncertain read.
Can this integrate with our existing MES, ERP, or dealer management system?
Yes, through your existing APIs and database views, not a rip-and-replace. The same multi-step reasoning-and-validation pattern we shipped for Synera's engineering-workflow platform applies to querying production data directly, without altering the underlying schema.
What is the typical path from pilot to a production agent?
A scoped Proof of Value (one workflow, real data, a working agent) typically lands within a few weeks. Full production rollout with monitoring and an operator handoff follows the same TriStorm phases as any other Vstorm engagement.
Start with one workflow
Map one production workflow worth automating
A 30-minute call identifies data access, integration points, and a realistic path to a working agent your engineering team will trust.