In one person's head
One estimator knows what that part actually runs at. One scheduler knows which customer will accept a late ship. When they are on vacation, everyone guesses. When they retire, it is gone.
Every absence becomes a fire drill
Answers from your data, not the internet
AI chatbots for manufacturing, built on your own data
A general chatbot knows nothing about your business. We build one that reads your live ERP data and your own documents, so when someone asks what a customer ordered last time, the answer is right and they can check it.
- Live ERP data
- Your documents
- Cited sources
- Your infrastructure
Grounded in your systems. Sources on every answer. Deployed in your accounts.
The gap
Your people already know where the answers are. That is the problem.
Knowledge in a manufacturing business lives in three places, and none of them scale.
In a folder nobody can search
Work instructions, prints, certs, spec sheets, and the email where the customer approved the deviation. All of it exists. Finding the right one takes someone who already knows where it is.
Minutes per lookup, dozens of times a day
Behind four screens in the ERP
The answer is in the system. Getting it means knowing which screen, which filter, and which field actually means what. So people ask a person instead.
Two people interrupted per question
What we do
What we build
One assistant, pointed at the things your team actually asks about.
Grounded in live ERP data
Not a snapshot from last quarter. The chatbot reads current orders, inventory, jobs, and history at the moment someone asks.
In practice
Inside sales asks what a customer ordered last time and what it shipped at. The answer comes from live ERP records, with the order numbers attached so anyone can verify it in ten seconds.
Trained on your documents
Work instructions, travelers, prints, certs, spec sheets, policies, and the tribal knowledge that only exists in old email threads.
In practice
An operator asks how to set up a job they have not run in a year. The assistant pulls from your own work instruction for that part, not a generic answer, and links the document.
Sources on every answer
Every response links back to the record or document it came from. Nobody has to trust it blind, which is what makes people actually use it.
In practice
This is the difference between a tool people try once and a tool people keep. If an answer looks wrong, they click through, see why, and tell you. That feedback makes it better.
Wherever your team already works
In a browser, in Teams or Slack, on a tablet at the machine, or inside an app we built for you.
In practice
Nobody adopts a tool that lives in a tab they have to remember to open. We put it where the question already gets asked.
Scoped to who is asking
Sales sees what sales should see. The floor sees what the floor should see. Costs and margins stay behind the same walls they already sit behind.
In practice
Permissions follow your existing roles rather than inventing new ones. If someone cannot see a margin field in the ERP today, the assistant will not show it to them either.
Customer facing, when it makes sense
The same foundation can answer for customers: order status, lead times, documentation, reorder history. With hard limits on what it can say.
In practice
A customer portal assistant that answers "where is my order" from real data, at 11pm, without anyone in the building. Scoped so it can only ever see that customer's records.
Not sure which of these matters most for your company? That is what the first call is for.
Under the hood
How we ground it in your reality
The quality of a chatbot is almost entirely the quality of what it can see.
- Live system access
- Through your ERP's supported interface, often via an MCP server, so answers reflect what is true right now instead of an old export.
- Your document corpus
- Indexed from wherever it already lives: file shares, SharePoint, the document module in your ERP. We do not ask you to reorganize anything first.
- Retrieval with citations
- The assistant retrieves the specific records and passages it needs, and shows them. That is what makes an answer checkable.
- Evaluation against real questions
- We collect the questions your team actually asks and test against them, so you find out it is wrong in testing rather than in front of a customer.
What we will not do
- We do not let it invent answers. When it does not know, it says so and points at who would.
- We do not send your documents or business data off for training. Your corpus stays yours.
- We do not ignore your permissions. Access follows the roles you already have.
- We do not hold your data. Everything is deployed to your infrastructure and your accounts.
2 to 4 weeks
from first call to something your team can use
Every answer
links back to the record or document behind it
0
of your data used to train anyone else's model
Systems
AI chatbots for the ERP you already run
We build this on any system we can read. These are the ones we work in most.
Questions
AI chatbots for manufacturing questions we get asked
A general assistant has never seen your order book, your work instructions, or your customer history. Ask it about your backlog and it will produce a confident, invented answer. What we build reads your live systems and your documents, and cites what it read, so answers are both correct and checkable.
No. Your business data and documents stay in your environment and are used to answer your questions, not to train anyone's model. This is usually the first question IT asks, and it is a fair one.
Two things reduce that to a manageable level. Every answer cites its source, so a wrong one is visible rather than silent. And we test against the real questions your team asks before it goes live, instead of finding out in production. It will still be wrong sometimes, the same way a new hire is. The citations are what make that survivable.
No. We work in Epicor Kinetic, SYSPRO, Global Shop Solutions, Made2Manage, and abas most often, and in Infor and Sage environments too. If your system exposes an API or a database we can read, we can ground a chatbot in it.
Both, and they are different builds. Internal assistants get broad access scoped by role. A customer facing one is locked to that customer's own records with hard limits on what it can discuss. Most companies start internal, because the stakes are lower while you build trust in it.
Adoption follows accuracy and placement. If it answers correctly and lives where the question already gets asked, in Teams or on the tablet at the machine, people use it within days. If it is a separate portal that is sometimes wrong, they will not, and no amount of training fixes that.
Let's talk
What question does your team ask a person twenty times a day?
Thirty minutes. You tell us what people keep having to look up. We tell you what it would take to make that answer available to everyone, instantly.
No commitment and no pressure
We will tell you straight if we cannot help
The invite lands in your calendar right away
From first call to live in 2 to 4 weeks
Loading available times
Read next
Other things we build
AI for manufacturers
A guided way into AI for small and mid-sized manufacturers. We start with what is already costing you, prove it, then build from there at your pace.
Data foundations for AI
Getting your data into a shape AI can actually use. The unglamorous part that decides whether everything after it works.
MCP servers for ERP
A safe, scoped connection between your ERP and Claude, ChatGPT, or Copilot, so your team can ask questions in plain English and get answers from live data.
Manufacturing dashboards
Live dashboards built from your ERP and the spreadsheets beside it. One set of numbers, on a wall screen and a phone.
Everything we build