01
Text-to-workflow generation
An agent reads design intent and assembles a complete parametric engineering workflow automatically — replacing hours of manual node-wiring with a reviewed draft.
Agentic AI in manufacturing
Agents that work the production floor .
We build agents that turn engineering intent into complete parametric workflows and read production data directly from MES and ERP systems. Every generated workflow or query carries the validation steps a plant engineer will actually sign off on.
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
Most manufacturing AI stalls at a single bolted-on chatbot
Production data lives across MES, ERP, PLM and SCADA systems that were never built to talk to each other, and the judgment that resolves conflicts between them sits with a senior engineer rather than a database. A single LLM call against that sprawl, one prompt turned into one SQL query or one design brief turned into one workflow, breaks quietly the moment the schema spans many views or the design intent has more than one valid engineering path. Decomposing the task into checked steps, each validated before the next runs, is what catches a wrong join or a bad parameter before it reaches the floor.
every step validated before the next runs
Use cases
Where agents earn trust in manufacturing operations
Workflows with a clear engineering or production boundary, and a validation step before anything reaches the floor.
02
Natural-language production queries
A multi-step agent-graph translates plain questions into verified SQL across MES and ERP schemas, instead of a single-shot query that silently returns the wrong join.
03
Predictive maintenance triage
An agent reads equipment sensor streams, flags anomalies against maintenance history, and drafts a work order for a technician to confirm — not to approve.
04
Quality deviation flagging
Cross-references inspection data against spec documents to flag deviations for engineering sign-off, compressing a manual paperwork trail into one reviewable record.
The cost of manual engineering work
Where engineering and production workflows lose hours today
These numbers come from real shipped agentic AI engagements. Synera's is manufacturing-adjacent engineering software; the Schmitt-Thompson and Mixam figures are not manufacturing deployments — they show the same staged-validation and multi-agent orchestration mechanism in healthcare and retail. Every figure below links to the case study behind it.
Agents do not replace engineering judgment — they compress the manual wiring, querying and cross-checking that sits before it, with a validation step before anything reaches the floor.
Manual workflow setup per engineer
2 hrs
Each complex parametric workflow on Synera's platform meant an engineer manually wiring nodes by hand.
0 hallucinations
Hallucination events across 329+ clinical scenarios
Not a manufacturing deployment, but the same staged retrieval-and-validation mechanism a production database query needs to avoid a silently wrong join.
95.4%
Success rate for a production multi-agent advisor
A three-agent product advisor guiding customers through print-order configuration — 15 tools working against more than a billion product combinations.
Client results
Proof from production manufacturing deployments
Our directly-published manufacturing proof is Synera's engineering-workflow platform. The other two cases show the same staged-validation and multi-agent orchestration mechanism in healthcare and retail — not manufacturing deployments.
“Beyond saving engineering teams hundreds of valuable hours each quarter, Synera aims for the easiest to use AI agent platform to make the AI transformation for engineers as smooth and frictionless as possible.”
0%
hallucinations in generated workflows
Mixam · Print-order configuration
A three-agent product advisor guiding customers through print-order configuration — 15 tools working against more than a billion product combinations.
95.4%
Success rate in workflow results
Delivery path
From workflow audit to production agents on the shop floor
TriStorm keeps engineering validation and delivery aligned — integration risk surfaced before full build commitment.
Scope the workflow and system boundaries
We audit the target engineering or production workflow, the MES/ERP/PLM schema it touches, and existing data quality — ranking use cases by impact and integration risk.
- Workflow & systems audit
- Data source map
- Prioritised use case
Build, test and validate
We implement against real production data shapes and design-intent samples, with an evaluation suite scored against known-good outputs before anything reaches the floor.
- Working prototype
- Evaluation suite
- Escalation rules
Deploy with monitoring and support
Production rollout with monitoring, audit logging on every generated workflow or query, and a structured runbook handoff to engineering and operations.
- 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 and production 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 manufacturing, answered
Where does agentic AI actually fit on a shop floor that already runs MES, ERP, and SCADA?
In the workflows that sit between those systems today — engineering intent that becomes a parametric workflow, a production question that becomes a database query, a sensor reading that becomes a work order. We do not replace the MES or ERP. We build the agent layer that reasons across them and hands a validated result to an engineer or operator.
How is this different from the automation rules we already run on the line?
Rules-based automation repeats a fixed sequence and breaks the moment a part, sensor reading, or schema does not match its assumptions. An agent reasons through the exception — checks a second source, retries a different path, or escalates, instead of stopping the line or silently returning a wrong answer.
What does agentic AI actually generate for an engineering team?
For Synera, a text-to-workflow agent reads design intent and assembles a complete parametric engineering workflow automatically — work that used to mean an engineer manually wiring nodes by hand. Generating one validated workflow went from about two hours to about three minutes. Their published library of 1,000+ existing workflows was what we transformed into the training dataset; that figure describes the input we had to work with, not the number of workflows the agent has since produced.
Can an agent answer questions directly against our production database?
Yes, through a hybrid agent-graph rather than a single LLM call — a graph of checked steps catches a bad join instead of shipping it. We have not yet shipped that specific pattern inside a manufacturing client; the closest reference is the zero-hallucination validation pipeline we built for Schmitt-Thompson Clinical Content, where a healthcare guideline agent was checked scenario by scenario, 329+ cases, before going live. The same staged-validation mechanism is what a production database query needs.
How do you handle traceability for an ISO-audited or regulated process?
Every step an agent takes is logged — what data it read, what it generated, and who reviewed it before it reached production. We design that audit trail alongside the agent, not after a quality audit asks for it.
What happens when the agent is not confident in a generated workflow or query?
It escalates rather than guesses. Confidence thresholds and validation gaps route the output to a human engineer with the reasoning attached — the same escalation pattern behind Synera's production system and the staged-validation pipelines we have shipped elsewhere.
Can this integrate with our existing PLM, MES, or ERP without a rip-and-replace?
Yes, through your existing APIs and data infrastructure. Synera's platform integrates with systems that were already in place — the agent is a new layer, not a system replacement, and we bring the same integration discipline to a manufacturing engagement.
What is the typical timeline to a working system?
A scoped Proof of Value (one workflow, real data, a working agent) typically lands in around three weeks. Full production rollout with monitoring and an operator handoff follows the same TriStorm phases as any other engagement.
Start with one workflow
Map one engineering or production workflow worth automating
A 30-minute call identifies the systems involved, the validation steps required, and a realistic path to a working agent your engineering team will trust.