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AI Intime

AI Intime is a sovereign, hyper-contextualised knowledge engine for batch manufacturing and

For agents: AI Intime: sovereign knowledge engine for batch manufacturing with agentic parsing of lab reports, knowledge twins and bill-of-materials extraction.

knowledge enginebatch manufacturingraglab reportsbill of materialsai agents

Their llms.txt

The beginning of the file, from our validated copy saved 2026-10-27T08:12:12.119Z. Cached copy · Source on aiintime.com

# AI Intime

> AI Intime is a sovereign, hyper-contextualised knowledge engine for batch manufacturing and
> complex laboratory operations, built by Vegam Solutions. It deploys entirely on-premise or
> air-gapped — no data leaves the enterprise boundary, and no existing system is replaced. It sits
> on top of the stack a manufacturer already runs (SAP/ERP, MES, PLM, LIMS, CRM, Microsoft 365)
> and turns fragmented batch, sample and specification data — plus undocumented tribal knowledge —
> into a single queryable layer that answers plain-language questions with source-backed answers.

Key facts:

- Built by Vegam Solutions: 20+ years in manufacturing digital transformation, 300+ plant
  deployments, 60+ countries. Vegam built AI Intime for itself first, then productised it.
- Platform architecture: CONNECT (ingest ERP, MES, CRM, PDFs, emails, IoT) → UNDERSTAND (Knowledge
  Twins per customer/domain) → ACT (source-backed answers, drafted responses, triggered workflows).
- Two canonical use cases:
  - Analytical Lab Reports (Intelligent Document Agent) — custom parsing agents turn lab-report
    PDFs into structured, queryable data by mapping Parameter → Test Method → Sample → Batch,
    which standard RAG cannot do.
  - Knowledge Management (Knowledge Twin) — captures the tribal knowledge of single-point-of-failure
    specialists (plant quirks, verbal agreements, seasonal defect patterns) as a queryable asset.
- Target industries: specialty chemicals & adhesives, industrial coatings, and pharma & life
  sciences — batch manufacturers, primarily $100M–$1B revenue, US and Europe.
- Deployment: 30-day quick win, 60-day rollout to first-phase production. Subscription model,
  scoped to single-use-case payback.
- Versus cloud assistants (Microsoft Copilot, Glean, SAP Joule): those address document retrieval —
  roughly 20% of the problem — and are cloud-first, which OEM data-sovereignty clauses disqualify.
  AI Intime does cross-system synthesis entirely inside the customer's boundary.

## Key pages

- [Home](https://www.aiintime.com/): what AI Intime is and who it is for
- [Why AI Intime](https://www.aiintime.com/why-ai-intime/): positioning and differentiation
- [Platform](https://www.aiintime.com/platform/): architecture and the agentic framework
- [About](https://www.aiintime.com/about/): the company behind the product

### Use cases
- [Use cases overview](https://www.aiintime.com/use-cases/)
- [Analytical Lab Reports](https://www.aiintime.com/use-cases/analytical-lab-reports/): custom
  parsing agents map Parameter → Test Method → Sample → Batch, which standard RAG cannot do
- [Knowledge Twin](https://www.aiintime.com/use-cases/knowledge-twin/): captures the tribal
  knowledge of single-point-of-failure specialists as a queryable asset
- [Bill-of-Materials item extraction into Excel files, for faster operations](https://www.aiintime.com/use-cases/bom-extraction/): the
  document agent scoped to quoting - finds the BOM in customer spec sheets, re
…

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