Knowledge Engine
Your Facility's Knowledge. Searchable in Seconds.
RAG-powered semantic search across your facility's own documents. Not the internet. Not general AI. Your facility's actual institutional knowledge.
Why not just use ChatGPT?
General AI (ChatGPT, Copilot, etc.)
- → Answers from the general internet
- → No access to your SOPs or repair logs
- → No citations, cannot be verified
- → Can hallucinate plausible-sounding wrong answers
- → May suggest procedures not valid for your equipment
Vestran Knowledge Engine
- → Answers only from your uploaded documents
- → Knows your facility's specific SOPs and history
- → Every answer cites source document and section
- → If the answer isn't in your docs, it says so
- → Procedures are validated against your facility's practice
How it works
Upload your facility documents
PDF, DOCX, TXT, and Markdown: SOPs, repair logs, OEM manuals, engineering notes, and process specs. No template or format required.
AI extracts and indexes the content
Vestran chunks each document into semantic sections, generates vector embeddings, and indexes them in your facility's private knowledge base. No document leaves your workspace.
Technicians search in natural language
A technician types "why is the CVD chamber throwing thermal alarms" and gets the relevant passages from your facility's own repair log, not generic internet results.
Every answer cites its source
The AI response shows which document and section it came from. Engineers can trace and verify any answer back to its source. No hallucination from general knowledge.
Expert Knowledge Capture
1 in 3 U.S. semiconductor workers is 55 or older. When they retire, their tacit knowledge retires with them.
Source: McKinsey, 2024 ↗Documents only capture what someone took the time to write down. But the most valuable knowledge is often what engineers never wrote: the non-obvious troubleshooting approaches, the equipment behaviors they knew from 15 years of experience, the workarounds that work when the SOP doesn't. Vestran's Expert Knowledge Capture sessions extract this before it's lost.
How a capture session works
- 1A structured AI-guided interview with the retiring engineer, typically 2–4 hours across two sessions
- 2The AI asks clarifying questions based on their specific role, tools, and experience
- 3Transcript is reviewed and organized into categories (equipment, process, troubleshooting, procedures)
- 4Final document enters the knowledge base and becomes immediately searchable by the entire team
- 5Future technicians can ask about the specific failure modes, workarounds, and non-obvious behaviors that engineer knew
Privacy and data isolation
Organization isolation
Your documents are only visible within your organization. No data is shared across customers. Each organization has a completely separate knowledge namespace.
No training on your data
Vestran never uses your facility's documents to train AI models. Your SOPs, repair logs, and expert knowledge are not shared with AI providers for training purposes.
Document removal
Documents can be removed from the knowledge base at any time. Removal deletes all associated chunks and embeddings, and the content is no longer searchable immediately.