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.

Contact UsSee how it works

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

MIT
50%+

of companies see zero ROI from their AI investments

PwC

The 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.

Generic AI tools who are amazing at writing emails and summarizing documents, don't just marginally fail here - it fails structurally.It can find the word "sodium."It cannot reliably link Parameter → Test Method → Sample → Batch; they can't tell you which test method produced it, which batch it belongs to, or how it compares across 400 other reports.

So analysts open PDFs one at a time and re-key by hand.

Prove us wrong ;-)
What a structural query looks like
Which reports contain Sodium and Chloride?
Approach
  1. Scans the entire report library for two parameters at once
  2. Not text matching - a structural query
  3. Across every report where both parameters were tested
  4. Regardless of which table or paragraph they sit in
The solution

Connect → Understand → Act

AI Intime is deployed on-prem / air-gapped - on top of the stack you already run - ERP, MES, PLM, LIMS, CRM, Microsoft 365 - entirely inside your enterprise boundary.

Connect

Ingests structured and unstructured signals: ERP, MES, CRM, PDFs, emails, IoT, wearables.

Understand

Get Hyper Contextual Knowledge Engines for respective use cases - learning context, history, specs and patterns.

Act

Answers in plain language and drafts responses, all source backed, you can trust every time, every day.

Explore the platform →
Why it's different

Five things generic AI can't do here.

01

Sovereign by architecture

Your data never leaves. Vaults scoped the way you choose - per plant, per team, per project - and each OEM account isolated with verified access controls, defensible to your customers’ auditors.

02

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.

03

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.

04

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.

05

A platform, not a point tool

A full agentic framework - intent recognition, planning and reasoning, execution and validation, memory and guardrails - on one 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 Management

Knowledge 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
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 discovery session is a working conversation about your lab reports, your tribal knowledge, and your sovereignty constraints - not a slide deck.

Contact UsRun the exercise yourself

On-prem. Your data never leaves your boundary.