AI for manufacturing and industrial businesses
AI for Manufacturing and industrial businesses
The documents your quoting depends on are exactly the documents you cannot put into a public AI tool.
The problem, specifically
A manufacturer's competitive position is partly in its specifications — tooling, tolerances, material choices, formulations, hardware compatibility across product lines. That material is both the thing staff most need to search quickly and the thing that must never end up in somebody else's training data.
The practical cost shows up in quoting. A sales engineer answering a customer question about compatibility across multiple material lines is hunting through spec sheets, and the time that takes is time the customer is waiting.
The paperwork side is the familiar problem: supplier invoices, packing lists and certificates arriving as scans and getting keyed in by hand.
What we deploy
Private AI
Secure retrieval across spec sheets, drawings, technical documentation and past quotes, running inside your own network so proprietary detail stays proprietary. This is the core of most manufacturing deployments we do.
Felican IDP
Supplier invoices, packing lists and certificates read into structured data rather than retyped.
Chat AI Assistant
Helps distributors and installers find the right part on your site, which is usually the hard part of a hardware catalogue.
What makes this different in your trade
Specifications never leave the building
This is the entire reason manufacturers end up on a private deployment rather than a subscription. Tooling detail and formulations put into a public model are disclosed, whatever the vendor's retention policy says this quarter.
Quoting speed is where it shows first
Compatibility and spec lookups across material lines, answered in seconds from your own documentation, with the source document cited so the engineer can verify rather than trust.
Retrieval is scoped by role
Sales needs the catalogue and compatibility data. Engineering needs tolerances and drawings. Those are different scopes, and mixing them is how confidential detail leaks internally.
Answers cite their source
For specification work an uncited answer is useless — nobody will act on a tolerance figure without knowing which document it came from. Retrieval returns the source alongside the answer.
Questions people actually ask
Before you call
Can it read our drawings?
Text, annotations and structured data in drawings, yes. Interpreting geometry from a CAD file is a different problem and we will say so rather than overpromise.
How do we stop it exposing one customer's specs to another?
Scoped retrieval with permissions mapped to your existing groups. Where custom work is involved this is the first thing configured and the first thing tested.
Is our data used to train a model?
No. Self-hosted means there is no vendor to send it to, which is the difference from every subscription AI product.
Talk to someone who knows manufacturing
A short call, and an honest answer about whether this is worth doing for your business.
