A quality engineer asks a public AI tool why a batch of coils drifted out of tolerance, and the tool answers confidently — using nothing but general knowledge about rolling mills, because it has never seen your recipes, your SOPs, or last quarter's CAPA log. That answer sounds right and is worth nothing. On-premise retrieval-augmented generation fixes this by keeping every recipe, SOP, CAPA record and live SPC tag inside your own walls while still letting the AI reason over all of it.
The AI Only Knows What Your Plant Lets It See
On-premise RAG grounds every answer in your actual recipes, SOPs, past CAPA records, customer specs, and live SPC tags — nothing leaves the site, and every response comes with a citation back to the source document.
What Happens When AI Guesses Instead of Retrieves
A generic language model was never trained on your specific tolerances, your specific supplier substitutions, or your specific corrective actions from last March.
Five Sources, One Grounded Answer
Retrieval only works if the index actually contains the documents an operator, quality engineer, or supervisor would otherwise dig through manually.
Recipes
Every active and historical recipe version, including the parameter changes that were made between revisions and who approved them.
SOPs
Standard operating procedures indexed at the paragraph level, so a question about a single step returns that step, not the whole document.
Past CAPA
Closed and open corrective action records, searchable by defect type, line, and root cause, so the same failure is never re-diagnosed from scratch.
Customer Specs
Contract-specific tolerance bands and acceptance criteria, kept separate per customer so the AI never mixes one buyer's spec with another's.
Live SPC Tags
Real-time statistical process control data streamed directly into the retrieval layer, so an answer about "right now" is actually about right now, not a stale export from the last shift change.
Grounded Answers Beat Confident Guesses
iFactory's on-prem retrieval layer indexes your recipes, SOPs, CAPA history, customer specs, and live SPC tags — then shows the exact source behind every answer the Plant Copilot gives.
The Difference Shows Up the First Time It's Wrong
A public model and an on-prem RAG copilot can sound identical right up until someone checks the answer against the actual document.
| Capability | Public / Generic AI | On-Prem RAG Copilot |
|---|---|---|
| Source of the answer | General training data | Your recipes, SOPs, CAPA, specs |
| Where data lives | Sent to an external service | Stays inside the plant network |
| Citation of source | Rarely provided | Shown with every answer |
| Awareness of live SPC | None | Streamed in real time |
| Customer spec separation | Not applicable | Isolated per customer contract |
What Operators Actually Ask the Copilot
These are the recurring lookups that used to mean walking to a filing cabinet, calling a supervisor, or paging through a binder on the line.
Why did this coil fail spec?
The copilot cross-references the live SPC reading against the customer's exact tolerance band and surfaces the closest past CAPA for the same defect signature.
What changed in this recipe last month?
A version-by-version diff of the recipe, including who approved each parameter change and the reason logged at the time.
What's the correct SOP step here?
The exact paragraph from the current SOP revision, not a summary, with a link back to the full procedure for context.
Has this defect happened before?
A search across closed CAPA records for matching defect codes, with the corrective action that resolved it previously.
How the Retrieval Layer Gets Stood Up On-Site
Nothing about this requires sending documents to a third party or opening a path out of the plant network.
Inventory the Document Sources
Catalog recipes, SOPs, CAPA records, customer specs, and the SPC tag streams that need to feed the retrieval index.
Build the On-Prem Vector Index
Index every document locally, at the paragraph and parameter level, on hardware that never leaves the plant's network boundary.
Connect Live SPC Streams
Pipe real-time statistical process control tags into the same retrieval layer so current readings sit alongside historical documents.
Validate Citations Against Source
Run the copilot against known questions and confirm every cited source actually supports the answer before rolling it out floor-wide.
On-Prem RAG — Questions Answered
What AI and IT teams typically ask before scoping a retrieval deployment for a regulated plant.
Q: Does any document or SPC data ever leave the plant network?
No, the entire retrieval index, the vector database, and the model inference all run on infrastructure inside the plant's own network boundary. Recipes, SOPs, CAPA records, and live SPC readings are indexed and queried locally, and no document content is transmitted to an external API or cloud service. This is the core reason on-prem retrieval is chosen over a hosted AI assistant for regulated manufacturing environments. You can book a demo to see the on-prem architecture reviewed with your IT and security team directly.
Q: How does the copilot know which customer spec applies to a given order?
Customer specifications are indexed separately and tagged to the specific production order and contract they belong to, so the retrieval layer only pulls the tolerance band that matches the order being discussed. This separation prevents one customer's acceptance criteria from ever being surfaced against another customer's product, which matters both for accuracy and for contractual confidentiality. The tagging happens automatically when specs are loaded rather than requiring manual sorting. Our support team can walk through how your existing spec files map into this structure.
Q: What happens if a question can't be answered from indexed sources?
The copilot is designed to say so rather than fill the gap with a general guess, which is the behavior that most differentiates grounded retrieval from an open-ended language model. If no recipe, SOP, CAPA record, or SPC reading supports an answer, the response states that no matching source was found instead of generating a plausible-sounding but ungrounded reply. This keeps the tool trustworthy for the exact situations where a wrong answer would matter most, like a quality hold or a safety-adjacent procedure.
Q: How current is the SPC data the copilot references?
Live SPC tags are streamed into the retrieval layer continuously rather than batch-loaded on a schedule, so a question asked mid-shift reflects readings from that same shift, not an overnight export. This matters most for the exact moment an operator is trying to decide whether a drifting parameter needs an immediate intervention. Historical SPC data remains searchable alongside the live stream for trend and CAPA-matching purposes.
Q: Can the retrieval index be updated as SOPs and recipes change?
Yes, the index re-indexes automatically whenever a recipe is revised or an SOP is approved through the normal document control process, so the copilot always references the current approved version rather than a stale copy. Superseded versions remain searchable for audit and history purposes but are clearly marked as prior revisions. This keeps the retrieval layer aligned with whatever document control workflow your plant already runs.
Answers Your Plant Can Actually Trust
Stop letting a generic AI guess about your recipes and tolerances. iFactory's on-prem retrieval layer grounds every answer in your own recipes, SOPs, CAPA history, customer specs, and live SPC data — with a citation attached every time.
