Runs inside your infrastructure
The engine is deployed within your enterprise boundary. There is no egress of batch records, lab results, formulations or correspondence to a third-party cloud.
Why AI Intime
Sovereignty isn't a constraint. It's the moat.
Most enterprise AI fails here for a reason that has nothing to do with model quality. It fails because it cannot legally see the data, and because retrieval alone was never the problem.
The disqualification
Cloud AI is ruled out before the demo.
OEM supplier quality agreements include explicit data confidentiality clauses. Data residency requirements sit on top of them. For a regulated batch manufacturer, sending batch records, formulations or lab results to a third-party cloud is not a risk to be managed - it is a clause to be breached.
Which is why so many AI pilots in this industry die in legal review rather than in evaluation. The architecture was wrong before the first query was typed.
What you keep control of
Everything stays where your policies already reach.
The questions that usually stop an AI project - where does the data go, who can see it, what do we tell the auditor, what happens at renewal - have architectural answers here, not contractual ones.
Vaults, scoped your way
Per plant, per team, per project - you choose the isolation boundaries. Data does not pool into a shared tenant you cannot inspect.
Account-level isolation
Each OEM account is isolated with verified access controls - the kind that survives a customer audit rather than a vendor assurance.
Model-agnostic
No lock-in to a single LLM provider’s terms or pricing. Swap the model without rebuilding the system.
Nothing replaced
No system of record is touched. Your ERP, MES, PLM, LIMS, CRM and Microsoft 365 stay exactly as they are.
Governance built in
Policy and governance engine, safety and alignment guardrails, and memory/context management are part of the framework - not bolted on afterwards.
On-prem does not make governance harder. It makes it demonstrable - the deployment sits inside the perimeter, access controls you already operate, and an audit trail you can show a customer rather than forward from a vendor.
The alternatives
What the obvious options actually solve.
Microsoft Copilot · Glean · SAP Joule
Solves
Document retrieval within individual applications - roughly 20% of the problem.
The gap
They cannot do cross-system synthesis: a SAP batch record → a LIMS test failure → a QMS CAPA → an email → a document from another plant two years ago.
The blocker
Cloud-first. OEM data-sovereignty clauses disqualify them before technical evaluation begins.
Databricks Genie
Solves
Conversational querying over structured data already sitting in a Databricks lakehouse.
The gap
It assumes the data is already centralized, cleaned and modeled - which is precisely the work batch manufacturing and lab operations have not done.
The blocker
Also cloud-first, so it inherits the same sovereignty disqualification.
AI Intime does the connect-and-structure work the others assume is already done - entirely on-prem.
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.
On-prem. Your data never leaves your boundary.