Korber WMS + HighJump + private AI
AI for Korber WMS and HighJump: Make a Long-Lived Warehouse System Easier to Question
Short answer
AI for Korber WMS, including installations that descend from HighJump, connects a private model to warehouse inventory, tasks and shipments through a read-only database replica, the system's integration tools or scheduled extracts, and joins them with ERP data. The edition and the amount of customisation decide the path, so the first step is an inventory of what you actually run.
- ERP
- Korber WMS, HighJump WMS
- Industries
- Manufacturing, Distribution, Electronics, Aerospace
- Written for
- Director of Logistics
Korber's warehouse management software has roots in several acquisitions, HighJump being the best known in North America. Many installations have been running for a decade or more, often with substantial customisation: bespoke RF screens, custom labels, tailored interfaces to the ERP and to carriers. The result is a system that works but that few people fully understand.
That is where AI is most useful, and where it is most dangerous if done carelessly. A model that answers from a standard schema will be wrong about a system that has been bent to fit the business. The integration has to start with what is actually in your database and your customisations, and it has to show its working so staff can check an answer.
Korber continues to develop its portfolio, and any AI features in the version you run should be assessed first. A private layer fits when the answers require joining the WMS to the ERP, when data policy keeps warehouse and customer data off external AI services, or when you are weighing a migration and need a clear inventory of what the old system does.
This page covers how a read-only, privately hosted layer connects to Korber and HighJump-lineage installations, which warehouse questions it answers well, and what to ask a vendor before connecting a model to a live operation.
What usually gets in the way
The problems we hear most from director of logistics teams running Korber WMS.
Heavy customisation hides real behaviour
Custom screens, scripts and interfaces mean the standard documentation no longer describes your system, and the people who know the differences are few.
Version age limits integration options
Older installations may have limited modern APIs, so integration often relies on database access or file-based interfaces.
Carrier, label and EDI interfaces are brittle
Interfaces built years ago fail quietly, and a failed ASN or label message may only surface when a customer complains.
Migration decisions lack an honest inventory
Choosing whether to upgrade, move to a newer Korber product, or replace the WMS needs a clear list of what is customised and used.
Warehouse data rarely joins ERP data in reports
Answering why a customer order is late needs WMS and ERP facts together, which standard reports seldom provide.
Where AI earns its place in Korber WMS
Each use case names the ERP objects it reads or writes, so your ERP team can judge the integration effort before anyone commits budget.
Inventory and order questions in plain English
Ask for on-hand by location, held stock, or the status of an order and get an answer from current data with the query shown.
Touches: Inventory by location and container, orders and lines, holds, allocations
Outcome: Replaces many routine report requests with self-serve answers.
Interface failure triage
Group failed ASN, label, carrier and ERP interface messages by cause and affected shipments.
Touches: Interface and staging tables, shipment records, EDI or file logs
Outcome: Finds the failures that actually block shipments sooner; fixing them is still an engineer's job.
Customisation inventory
Index custom scripts, screens and interface specs so an analyst can ask what is customised and what each piece does.
Touches: Custom script and screen source, interface specifications, configuration exports
Outcome: Produces the starting inventory for migration planning; completeness depends on source access.
WMS to ERP reconciliation
Compare WMS and ERP balances for flagged items and propose likely causes from receipts, adjustments and interface errors.
Touches: WMS inventory transactions, ERP balances, adjustments, error logs
Outcome: Shortens reconciliation; adjustments are posted by people.
Pick performance and short-pick analysis
Identify recurring short picks and link them to location setup, replenishment timing and inbound delays.
Touches: Pick history, replenishment records, location master, receipts
Outcome: Surfaces patterns for the operations manager to act on; no automatic re-slotting.
Order readiness for customer service
Customer service asks where an order is and gets a joined WMS and ERP status with a drafted reply.
Touches: Order status, picks, shipments, carrier events, ERP order header
Outcome: Cuts interruptions to warehouse leads for status questions.
Migration gap analysis
Compare current configuration and customisation to a target platform's capabilities and produce a first gap list.
Touches: Configuration, custom code, interface inventory
Outcome: Gives the project team a first-pass list to verify, not a finished plan.
Reference architecture
The layer connects read-only to the WMS database replica or to the integration tools and extracts the installation supports, joins that data with ERP data in a store you control, and serves a private model with full query logging.
- 1
WMS connector
Read-only access to a database replica or reporting copy, or scheduled extracts and the system's integration tools, depending on edition. Production is not queried directly.
- 2
ERP connector
Reads orders, receipts and item data from your ERP so warehouse facts can be tied to demand.
- 3
Semantic layer
Maps standard and custom tables, statuses and attributes to business terms, built from your actual schema.
- 4
Model serving
Open-weight model served with vLLM or Ollama on customer GPUs or in a private tenant, with retrieval over data, specs and custom code.
- 5
Governance and audit
Queries and sources logged per user; human approval on any proposed write.
Integration notes for your ERP team
- Identify the exact product, version and database platform before anything else; HighJump-lineage and newer Korber products differ.
- Use a read-only replica or reporting copy; do not point the model at the production database.
- Map custom tables, statuses and attributes explicitly in the semantic layer, built from the real schema rather than a generic one.
- Interface and staging logs for ERP, carrier and label messages are among the best sources for triage questions; confirm what they record.
- Index custom scripts and screen definitions read-only for behaviour explanations, with a developer verifying important conclusions.
- Check any AI features in your Korber version first and avoid rebuilding what it already does.
Deployment options
Air-gapped on-prem
Database-backed installations in controlled networks, including defense and aerospace suppliers.
All components on customer hardware reading a replica; no outbound path.
Private or sovereign cloud
Customers on hosted or newer Korber deployments.
Dedicated tenant under your agreement with private connectivity.
Hybrid
Sites with different WMS generations.
Federated query layer with per-site residency and role mapping.
Compliance and data control
How the architecture supports your obligations. Certification and accountability stay with your organisation; the design keeps the evidence straightforward.
ITAR and EAR (customer-determined)
A customer-hosted architecture lets the customer's own program control access to controlled part data. Netray holds no export registration.
FDA and GxP lot traceability (where applicable)
Read-only access with query logs supports audit trails; records of record remain in the WMS and ERP.
GDPR and regional privacy
Consignee and contact data can be masked unless a role needs it.
Customer security reviews
Logging, role mapping and no external model egress support questionnaire answers.
How an engagement runs
Phase 1 . 2-4 weeks
Discovery
- -Product, version and customisation inventory
- -Data path decision
- -Ranked questions
- -Deployment recommendation
Phase 2 . 6-8 weeks
Pilot
- -Read-only replica or extract feed
- -3-4 use cases live
- -Accuracy review
- -Logging validated
Phase 3 . 4-6 weeks
Production
- -Cutover with read-only accounts
- -Role mapping
- -Runbook
- -Handoff documentation
Phase 4 . Ongoing
Scale
- -More sites
- -Approval-gated agents
- -Model refresh
- -Quarterly review
Questions to ask any vendor, including us
A short list that separates real Korber WMS AI work from a chatbot demo.
- Which Korber or HighJump product and version do we run?
- Will the layer ever touch the production database?
- How are custom tables, statuses and attributes mapped?
- Can you show the query and sources for each answer?
- Where does the model run and what leaves our network?
- What AI features exist in our Korber version, and how do you avoid duplicating them?
- How is any write-back approved?
- What do we keep if we stop?
Frequently asked questions
Does this work with older HighJump installations?
Generally yes, usually through a read-only database replica or scheduled extracts, with your custom schema mapped explicitly. The first step is confirming the exact product, version and customisation level, since that determines what can be read safely.
Why not use a generic BI tool?
A BI tool is a good choice for fixed dashboards. A privately hosted AI layer adds natural-language follow-up questions, joins with the ERP, and explanations drawn from interface logs and custom code. Many customers use both.
Can AI help decide whether to migrate?
It can speed up the inventory of customisations, interfaces and configuration and produce a first-pass gap list against a target platform. A person verifies it and makes the decision.
Will it change warehouse data?
Not by default. It is read-only, and any proposed change is drafted and routed through your existing approval process.
How accurate are answers on a customised system?
Accuracy depends on how well your actual schema and statuses are mapped. We validate against known-correct answers in the pilot and show the query and sources so users can check any result.
What about carrier and label interfaces?
If the interface and staging tables record message status, the layer can group failures by cause and affected shipments. If they do not, discovery will flag the logging gap, which is worth fixing regardless of AI.
What hardware is required?
A small number of GPUs, or a private cloud tenant, is typically enough for a question-answering model. Exact sizing depends on concurrent users and the model class chosen in discovery.
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Plan it with numbers
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Talk it through with an engineer who knows Korber WMS
Bring one real question your team cannot answer from the ERP today. We will map the data path, the model, and where it runs, and tell you honestly if AI is the wrong tool for it.