How to add an AI chatbot to Infor LN
Short answer
Add an AI chatbot to Infor LN by pairing a chat interface with a retrieval and tool layer that reads LN data through ION API or a replica and your documents through an index. Surface it in a portal, Teams, or Infor OS, keep it read-only, and run the model on-prem if required.
LN users know the pain of navigating sessions to answer simple questions: where is this order, which warehouse holds this lot, who approved this change. A chatbot lets them ask in plain language, in the tool they already use, and get an answer with a link to the LN record behind it.
A customer or supplier-facing chatbot is a separate project from an internal one. Internal users need breadth and can be trusted with detail. External users need a narrow scope, strong identity checks and answers limited to their own orders.
Below is the design for both, built around grounding: every answer comes from a tool result or a retrieved document, with the source shown, and the bot says it does not know when it does not.
What teams use it for
Where is my order for customers
Authenticated customers ask about their own sales orders and shipments in a portal chat, and get dates and carrier references without calling customer service.
Supplier self-service
Suppliers ask which purchase orders are open and what the required dates are, and confirm dates through a defined workflow rather than free text.
Planner Q&A
Internal planners ask about stock, supply and demand for an item and get a short answer with the numbers used.
Shop floor how-to
Operators ask procedure questions answered from controlled work instructions and quality documents, with revision shown.
New starter guide to LN
Staff ask how to perform tasks in LN sessions and get answers from your own internal documentation, not generic guesses.
Quote and lead-time questions for sales
Sales staff ask for current lead times and availability of standard items while on a call.
Infor LN integration surface
- ION API: authenticated REST calls to LN services for status and lookup, with credentials held by the chatbot backend only.
- Read replica or Infor Data Lake: preferred for analytical or high-volume questions so chat traffic never touches the transactional database.
- Infor OS and Ming.le style portal surfaces: where available, an embedded widget or application can host the chat window within the existing user experience. Exact embedding options depend on your Infor OS version.
- Identity: LN users and Infor OS identities, or an external identity provider for customers and suppliers, mapped to the data they may see.
- Documents: LN attachments, shared folders and document management, indexed with permission metadata for retrieval.
How it works
- 1
Define audiences and scope
Decide who the bot is for first. Internal planners, customers and suppliers need different tools, tone and permission models.
- 2
Build the tool layer
Create read-only functions for status, availability and document search, each returning concise structured data and a link to the LN record.
- 3
Index documents
Chunk and embed controlled documents with metadata for revision, owner and access group, and refresh on a schedule or on change.
- 4
Choose the model
Use a hosted model where policy allows, or an open-weight model on your own GPUs for private operation. Test on real questions in your language mix.
- 5
Add guardrails
Restrict topics, require tool or document evidence for factual claims, refuse out-of-scope requests and hand off to a human with context.
- 6
Embed it where people work
Place it in a portal page, Teams, or the Infor OS surface, and pass the authenticated user identity to the backend.
- 7
Measure and tune
Review unanswered and thumbs-down conversations weekly, add missing tools or documents, and track containment of routine questions.
Example flows
Customer order status
Trigger: Customer asks 'when will order 5521 ship?' in the portal.
- Backend confirms the logged-in customer owns that order.
- It calls the order status tool and reads line dates and shipment data.
- The bot answers with the date and shows only that customer's lines.
- If the date slipped, it offers to notify the account manager.
Result: A routine inquiry is answered immediately without a phone call.
Procedure question on the floor
Trigger: Operator asks how to rework a failed part on a given routing.
- Bot retrieves the current rework instruction for that item family.
- It answers with the steps and the document revision.
- If no instruction exists it says so and routes to the quality engineer.
- The unanswered question is logged for documentation owners.
Result: Operators follow the controlled procedure instead of memory or a stale copy.
Keeping data on-prem
The chatbot backend, the document index and the language model can all run inside your network. Use a retrieval index such as a vector store on your servers, an open-weight model served by vLLM or Ollama, and call ION API or the replica locally. For external users, place only a thin, authenticated front end in a DMZ and keep the model and data tiers behind it. Model sizing depends on concurrency and language needs, so test with realistic peak load before choosing GPUs.
Security checklist
- Authenticate every user and enforce row-level access in the tool layer; the model must never be the access control.
- For external audiences, restrict answers to the user's own orders and block cross-customer queries by design.
- Keep the chatbot read-only; route requests that need changes to a human or a defined workflow.
- Log conversations with retention and privacy rules agreed with your data protection owner.
- Filter and treat uploaded files and retrieved text as untrusted to limit prompt injection.
Ways to get it done
Vendor chat or assistant features within the Infor stack
Effort: Low, but limited to what is productised.
Simple internal help and navigation when you are on a version that offers them.
Build in-house with a framework and your own tools
Effort: Weeks to a first release, then continuing tuning.
Teams with engineers and an appetite to maintain retrieval and evaluation.
Partner delivery with private hosting, such as Netray
Effort: Scoped pilot of several weeks.
Sites needing on-prem models and external-facing safeguards.
Pitfalls
- !A bot that answers without evidence will invent dates; require a tool result for every fact.
- !Mixing internal and external scopes in one bot risks exposing internal data to customers.
- !Stale indexed documents make the bot confidently quote superseded procedures.
- !No human handoff frustrates users the first time the bot is wrong.
- !Measuring only deflection and not correctness hides quality problems.
Frequently asked questions
Can the chatbot live inside Infor OS?
Often it can be embedded as a web application or widget, but the available extension points depend on your Infor OS and LN versions. Check with your Infor administrator; a standalone portal or Teams deployment is a reliable alternative that needs no changes inside LN.
Can customers use it safely?
Yes, if identity and scope are handled in the backend. Authenticate each customer, map them to their own orders in the tool layer, keep it read-only and limit topics. Never rely on the model alone to keep one customer's data from another.
Does it need a large GPU?
Not necessarily. Modest concurrency with a mid-size open-weight model can run on a single server-class GPU, while heavier loads or large models need more. Retrieval quality and tool design matter more than model size for most LN questions.
What stops it making things up?
Grounding rules: the bot answers only from tool results or retrieved documents, shows its source, and says it does not know otherwise. You back this with tests on real questions and a review loop for failed conversations.
How is this different from Power BI Q&A?
Power BI Q&A queries a data model for charts and aggregates. A chatbot also handles documents, procedures and single-record lookups across tools, and can hand off to people. Many sites use both for different audiences.
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