InforERP PlatformUnited States

Distribution FACTS + AI

AI for Infor Distribution FACTS, Without Replacing the System

Short answer

Infor Distribution FACTS runs order entry, purchasing, and inventory for small and mid-size wholesale distributors on IBM i, and it does that job reliably at a scale where a full ERP replacement rarely pencils out. A private AI layer adds plain-language question answering and drafting on top of FACTS's existing data, so an owner or ops lead gets AI value without a system change, and without customer or pricing data ever leaving the business's own network.

ERP: Infor Distribution FACTS
Industries: Distribution, Wholesale
Written for: Owner

Discuss your Infor Distribution FACTS environment See use cases

As the owner of a distribution business running FACTS, you already know the system does what it needs to do: it processes orders, tracks inventory, and keeps the books, and it has done so for years without the drama of a bigger ERP implementation. What it does not do is answer a question in plain English, which means every AI conversation in your industry right now, about faster quoting, faster order status, faster supplier follow-up, sounds like it assumes a system you do not have.

The instinct many owners have at this point is to assume AI means a system replacement, a multi-year project few distributors this size can justify. That is not actually the case. FACTS already exposes its order, inventory, and purchasing data through IBM i database access that existing reporting tools use, and adding a private AI layer on top of that data does not require touching FACTS itself.

What changes in practice is who can get an answer without calling someone else. A CSR asking about a customer's order history, a buyer checking on a late shipment, a warehouse lead confirming stock across locations, all of these currently route through whoever knows the FACTS screens well enough. A grounded assistant answers those directly, and drafts the routine follow-ups and reports that eat time without adding much value each time they are done by hand.

This page covers what a private AI layer looks like specifically for a FACTS-based distributor: realistic use cases sized for a smaller operation, a deployment path that fits a modest IT budget, and the questions worth asking before committing to any vendor.

What usually gets in the way

The problems we hear most from owner teams running Infor Distribution FACTS.

One or two people know FACTS well

In a smaller distribution business, deep FACTS knowledge usually sits with one or two long-tenured staff, and their time becomes the bottleneck for anything outside a standard screen or report.

Order and inventory status questions interrupt everyone

Owners, sales staff, and warehouse leads regularly interrupt each other to answer status questions that FACTS technically already has the answer to, just not in a form anyone can quickly retrieve.

Supplier follow-up is entirely manual

Purchase order confirmations and expedite requests are drafted by hand for every late shipment, even though the underlying pattern, item, PO number, promised date, repeats constantly.

Reporting requires outside help or a rare skill

A question that falls outside a standard FACTS report usually means calling a reseller or consultant, or waiting on the one internal person who can write a query.

Budget for a full system replacement rarely makes sense

FACTS does its core job well enough that a full ERP replacement is hard to justify on ROI grounds, which leaves a gap between where the business is and where AI vendor pitches assume it is.

Where AI earns its place in Infor Distribution FACTS

Each use case names the ERP objects it reads or writes, so your ERP team can judge the integration effort before anyone commits budget.

Order and customer history in plain English

Sales and customer service staff ask about a customer's order history, current order status, and open balances without navigating FACTS inquiry screens.

Touches: FACTS order entry and accounts receivable files

Outcome: Cuts the time to answer a routine customer call from a screen lookup to a direct answer.

Inventory availability across locations

Warehouse and sales staff check on-hand and available quantities across locations without needing to know location-specific inquiry paths.

Touches: FACTS inventory and warehouse files

Outcome: Reduces ad hoc inventory questions that interrupt warehouse staff during busy periods.

Purchase order expediting drafts

The system drafts expedite or confirmation emails to suppliers for late purchase order lines, referencing the PO number, item, and promised date, for a buyer to review before sending.

Touches: FACTS purchase order and vendor files

Outcome: Turns a recurring manual task into a short review-and-send step for a buyer or owner.

Margin and pricing lookup

Sales staff and the owner check margin on a specific order or customer without pulling a full report, useful for quick pricing decisions on the phone with a customer.

Touches: FACTS pricing, cost, and order history files

Outcome: Supports faster, more confident pricing decisions during live customer conversations.

Ad hoc reporting without a consultant

The owner or a manager asks a question that does not map to an existing report, such as top customers by margin this quarter, and gets a direct answer grounded in FACTS data.

Touches: FACTS sales history and item master files

Outcome: Reduces reliance on a reseller or consultant for reporting requests that fall outside standard FACTS output.

New hire onboarding support

New staff ask the assistant how a specific FACTS process works, such as processing a return, instead of relying on the one experienced person in the office.

Touches: FACTS process documentation, historical transaction examples

Outcome: Shortens ramp time for new hires without adding to a stretched manager's training load.

Document search across FACTS and file shares

Staff search for a customer contract, a vendor agreement, or a prior credit memo alongside the related FACTS order or customer record in one query.

Touches: Document attachments or linked file shares, FACTS customer and order files

Outcome: Reduces time spent hunting for supporting documents when a customer disputes an order or invoice.

Reference architecture

The architecture is sized for a smaller distribution operation: it reads FACTS data through the same IBM i database access existing reports already use, and it keeps any write action behind a person's review, without requiring a large IT footprint.

1
ERP connectors
Direct, read-only DB2 for i database access to FACTS order, inventory, and purchasing files, the same path most existing reporting tools already use, with no change required to FACTS itself.
2
Data and semantic layer
A glossary maps FACTS's file and field naming to the terms staff actually use, sized to the business's specific item categories and customer structure rather than a generic distribution model.
3
Model serving
An open-weight model served on a single GPU workstation or small server, sized to a modest concurrent user count rather than the enterprise hardware a larger deployment would need.
4
Retrieval and agents
Retrieval-augmented generation grounds answers in current FACTS data; drafted outputs like a supplier follow-up email are reviewed by a buyer or the owner before being sent.
5
Governance and audit
FACTS user-level access is mirrored so a staff member's answers are scoped to what their FACTS sign-on already permits, with every query and draft logged for review.

Integration notes for your ERP team

Read access to FACTS data uses the same DB2 for i database connection existing reporting tools already rely on, requiring no change to FACTS configuration.
A scheduled extract or lightweight replica handles question-answering traffic so it never adds load to FACTS during order entry hours.
FACTS user-level access controls are mirrored into the AI layer so staff only see what their existing sign-on already permits.
Item categories, customer groupings, and any custom fields specific to the business are mapped during a short discovery pass rather than assumed from a generic distribution template.
The deployment is sized deliberately small at the start, a single server handles most single-location distributors, with a clear path to add capacity if the business grows.

Deployment options

Air-gapped on-prem

Distributors who want the AI layer running entirely on their own hardware, with no dependency on an internet connection for day-to-day use.

A single server or workstation in the office runs the model and connector, with FACTS data read locally and no outbound requirement for inference.

Private or sovereign cloud

Distributors who prefer not to manage hardware directly, or who run FACTS on a hosted IBM i partition already.

The model runs in a small, dedicated private cloud instance connected to the FACTS environment over a private link, sized and billed to match a smaller operation's usage.

Hybrid

Distributors with more than one location or who expect to grow into needing additional capacity over time.

Interactive question-answering stays close to the FACTS database at each location, while any heavier batch processing, such as a document re-index, can run on scheduled cloud capacity as volume grows.

Compliance and data control

How the architecture supports your obligations. Certification and accountability stay with your organisation; the design keeps the evidence straightforward.

Customer data-handling expectations

Even without a formal compliance mandate, distributors are increasingly asked by larger customers where their data is processed; keeping AI on infrastructure you control gives a straightforward answer.

PCI DSS adjacency

Where FACTS or a connected system handles payment card data, the AI layer is scoped to stay outside cardholder data flows entirely, reading order and pricing data without touching payment fields.

Basic data governance

Query and draft logs stay on infrastructure the business owns, giving a straightforward answer to any customer or insurer question about how AI tools handle business data.

State privacy law exposure

For distributors handling customer data subject to a state privacy law, keeping processing on infrastructure the business controls avoids adding a new third-party data processor to track and disclose.

Where Netray fits

ERPray

ERPray's lightweight connector to IBM i database access fits a FACTS environment well, giving plain-language question answering without requiring a large integration project.

Custom build

Distributors who want document search alongside FACTS data, or a very specific drafting workflow like rebate or freight reconciliation, typically add a small custom build on top of the same base connector.

How an engagement runs

Phase 1 . 1-2 weeks

Discovery

-Review of FACTS data structure and any custom fields in scope
-Short glossary mapping FACTS terms to plain language for the business
-Two or three pilot use cases picked based on daily pain, not theoretical value

Phase 2 . 4-6 weeks

Pilot

-Working connector to FACTS data in a test environment
-One or two use cases live for a small group of staff
-Access scoped to match existing FACTS user permissions

Phase 3 . 2-4 weeks

Production

-Deployment moved to production hardware or a small private cloud instance
-Approval step configured for any drafted supplier or customer correspondence
-Basic logging in place for query and draft activity

Phase 4 . Ongoing

Scale

-Additional use cases added as staff get comfortable with the assistant
-Capacity added if a second location or higher volume is added
-Periodic review to keep the glossary current as the business changes

Questions to ask any vendor, including us

A short list that separates real Infor Distribution FACTS AI work from a chatbot demo.

Does this touch FACTS itself, or does it only read data the way our existing reports already do?
Where does the model run, and does any customer or pricing data leave our office network?
What is the realistic total cost for a business our size, including hardware, not just a project fee?
How does the assistant handle staff-level access so someone only sees what they already can in FACTS?
What happens if we later grow into a second location?
Who do we call if the assistant gives a wrong answer, and how is that fixed?
How long before we see actual time savings, not just a working demo?

Frequently asked questions

Do we need to replace FACTS to get AI value?

No. A private AI layer reads FACTS data through the same database access existing reports already use, so it adds plain-language question answering and drafting on top of FACTS without requiring a system replacement.

Is this realistic for a business our size, not just large distributors?

Yes. The architecture is sized to match, a single GPU workstation or small server handles most single-location FACTS deployments, and the discovery and pilot phases are scoped shorter than a large enterprise engagement.

Will our customer and pricing data be safe?

In an on-prem or small private cloud deployment, inference happens on infrastructure you control, so customer and pricing data does not get sent to a public AI service as part of normal use.

What if we only have one or two people who really know FACTS?

That is one of the strongest reasons to add this layer: it gives other staff a way to get routine answers without interrupting the one or two people whose time is currently the bottleneck for anything outside a standard screen or report.

How much does this cost for a business our size?

Cost scales with the deployment size; a single-location distributor typically needs one GPU workstation or a small private cloud instance rather than enterprise-grade hardware, and the project scope is sized down to match during discovery.

How long does a pilot take?

A pilot for a business this size typically runs four to six weeks after a one to two week discovery pass, covering one or two use cases with a small group of staff before deciding on a full rollout.

What happens if we do eventually replace FACTS?

The connector would be rebuilt against the new system, but the model infrastructure and the use cases you have already validated carry over conceptually, so the investment is not wasted if your ERP strategy changes down the road.

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