How to add an AI chatbot to Epicor Kinetic

Epicor Kinetic integration surface

• BAQs through the BaqSvc OData endpoint for the structured answers the bot will cite.
• REST API v2 reads for single records, wrapped behind the bot's tool layer rather than exposed to the model.
• Epicor attachments and your document stores for the retrieval side. Index them with a vector store alongside permissions metadata.
• Identity: Kinetic users and groups for staff, and your portal's identity provider for customers and suppliers. Map each to allowed data.
• Epicor Application Studio can embed a custom panel or link out to a hosted chat page, depending on version and your customisation approach.

How it works

1. 1

   Pick the audience and the thirty most common questions

   Mine helpdesk tickets and email. The questions define your tools and your test set.

2. 2

   Build the grounded tool layer

   Each tool is a BAQ or REST read returning compact JSON with IDs and as-of timestamps.

3. 3

   Add document retrieval

   Index manuals and SOPs, tagging each chunk with who may see it. Retrieve only what the asker may access.

4. 4

   Choose the model and serving

   Open-weight models on vLLM or Ollama if data stays inside, or a hosted API for non-sensitive scopes.

5. 5

   Build the front end

   A web widget in your portal, a Teams bot or a kiosk page. Show the source records beside every answer.

6. 6

   Evaluate and monitor

   Run the test set weekly, review thumbs-down answers, and track the share answered with a cited record.

Security checklist

• Enforce the asker's identity and data scope on the server, never in the prompt.
• Use read-only access, and send any action, such as a date confirmation, to a human queue.
• Hide internal-only fields like cost and margin from customer and supplier modes.
• Add prompt-injection defences for retrieved documents and inbound text.
• Retain conversation logs per policy, with redaction of personal data.