Ingests structured and unstructured signals: ERP, MES, CRM, PDFs, emails, IoT, wearables.
Sovereign, hyper-contextualised knowledge engine · batch manufacturing & complex lab operations
"Which batches failed pH spec last month?"Don't Just Pilot AI. Operationalize It.
Sovereign, agentic AI for batch manufacturing and labs — on your existing systems, inside your own walls. No data leaves. No stack replaced.
Or skip the pitch — run the exercise yourself. No form, no gate.
The stakes
Most enterprise AI never leaves the pilot.
95%
of enterprise AI pilots fail to deliver measurable business impact
MIT50%+
of companies see zero ROI from their AI investments
PwCThe AI illusion looks impressive in a demo — and collapses under real enterprise complexity. In regulated batch manufacturing, it usually collapses before the demo.
The real problem
You already own the data. That was never the issue.
A lab report looks simple and isn't. One report carries results for multiple samples and multiple batches, across several test methods. Values are scattered between tables and narrative prose. Parameter names get reused and overloaded across templates. Standard RAG doesn't fail marginally here — it fails structurally. It can find the word "sodium." It cannot reliably link Parameter → Test Method → Sample → Batch, and it cannot filter or aggregate across a library. So analysts open PDFs one at a time and re-key by hand.
Generic tools — Microsoft Copilot, Glean, SAP Joule — solve document retrieval inside a single application. That's roughly 20% of the problem. They can find a document that mentions a parameter; they can't tell you which test method produced it, which batch it belongs to, or how it compares across 40 other reports. And they're cloud-first, so OEM and regulatory data-sovereignty requirements disqualify them before technical evaluation even begins.
What a structural query looks like
Which reports contain Sodium and Chloride?
ApproachScans the entire report library for two parameters at once — not text matching, but a structural query across every report where both parameters were tested, regardless of which table or paragraph they sit in.
The solutionConnect Understand Act Explore the platform →
Connect → Understand → Act
Agent networks deployed on top of the stack you already run — SAP, MES, PLM, LIMS, Salesforce, Microsoft 365 — entirely inside your enterprise boundary.
Builds Knowledge Twins per customer and domain — learning context, history, specs and patterns.
Answers in plain language and drafts responses, pulled live from source systems.
Why it's different01 02 03 04 05
Five things generic AI can't do here.
Sovereign by architecture
Your data never leaves. Each plant gets its own vault; each OEM account is isolated with verified access controls — defensible to your customers’ auditors.
Synthesis, not search
Connects a SAP batch record to a LIMS test failure, to a QMS CAPA, to a two-year-old email, to a verbal agreement made on an OEM site visit.
Operational intelligence, not document retrieval
Built for batch and sample-level granularity — parameter, test method, batch, deviation — and for the tribal knowledge that was never written down.
Direct answers, not dashboards
Ask in plain language. Get the answer, pulled live from your systems. No dashboard to build, no analyst queue to join.
A platform, not a point tool
A full agentic framework — with a roadmap from answering today to proactive monitoring tomorrow, on the same data foundation.
Where it starts
Two problems worth solving first.
Intelligent Document Agent
Analytical Lab Reports
Standard RAG cannot link Parameter → Test Method → Sample → Batch. A custom parsing agent can — turning static PDFs into structured, queryable data.
"Which samples failed pH specification in the last 30 days?"Read the use case →Knowledge ManagementKnowledge Twin
The specialist holding a decade of account intelligence is a single point of failure with a notice period. This captures what they know before it walks out.
"What are the known quality issues with BMW Mineral Grey?"Read the use case →Proof
We built it for ourselves first.
Vegam had the problem before we productized the answer: 20+ years of institutional knowledge trapped in emails, meeting notes and proposals — and cloud AI ruled out by our customers' terms. So the constraint became the architecture.
20+years in manufacturing digital transformation
300+plant deployments
60+countries
BASF CoatingsHenkelSIKA (MBCC)ArkemaJSW SteelLG ElectronicsSaint-GobainSKFUnilever
Flagship deployments behind the platform. Our current focus is mid-size batch manufacturers ($100M–$1B) across the US and Europe.
Who it's for
If any of these are true, this is live.
- A senior application engineer, PhD specialist or veteran plant SME just resigned or announced retirement
- A plant acquisition or facility transfer is creating knowledge transfer pain
- A cloud AI pilot was blocked by IT or Legal over data sovereignty
- Your lab team is manually re-keying data out of PDF test reports
- A supply chain compliance audit exposed documentation gaps
- Leadership has mandated an "AI strategy" — but cloud was ruled out
See it against your own data.
A strategy session is a working conversation about your lab reports, your tribal knowledge, and your sovereignty constraints — not a slide deck.
On-prem. Your data never leaves your boundary.