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AI ⇄ ERP

Azure OpenAI to Manhattan Associates WMS integration — real-time data sync

Keep Azure OpenAI and Manhattan Associates WMS in sync without custom scripts. Cut weeks of integration work, eliminate silent data drift, and give your team a single, reliable source of truth.

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Why teams connect Azure OpenAI and Manhattan Associates WMS

Ground Azure OpenAI on the customers, suppliers, items, and transactions that live in Manhattan Associates WMS, and write the classifications, scores, and extracted fields Azure OpenAI produces back onto those records in real time.

Azure OpenAI is a read-only source: Stacksync reads its data in real time and delivers it into Manhattan Associates WMS, so Manhattan Associates WMS always reflects the current state of Azure OpenAI — without exports, scripts, or schedulers.

An AI system does not keep customers, orders, or invoices the way an ERP does. What it keeps is derived: the vectors and metadata in a store, or the classifications, extracted fields, and generated text a model produces over records it was handed. Manhattan Associates WMS is where those source records actually live, across finance, operations, procurement, and inventory, and that data usually stays behind a strict API with many record types. Whatever Azure OpenAI produces, whether a category, a risk flag, an extracted value, or an embedding, only earns its keep when it lands back on the record in Manhattan Associates WMS where the business is run.

Stacksync connects Assistants, Vector stores, Deployments, Models in Azure OpenAI with Receipts, Locations & Zones, Returns, Appointments in Manhattan Associates WMS and keeps the two sides in sync in real time. Receipts, Locations & Zones, Returns, Appointments from Manhattan Associates WMS replicate into Azure OpenAI continuously, so retrieval, classification, and reasoning run against current ERP records instead of last night's extract, and the fields Azure OpenAI generates sync back onto the matching record in Manhattan Associates WMS. Mapping is field-level, matching is on identifiers you choose, and Stacksync handles the ERP's API limits and schema drift, so there is no extraction pipeline to build or babysit.

Because every AI-side item carries the key of the record it came from, results always resolve to the right customer, supplier, or transaction. Azure OpenAI reasons over the business as it actually is within seconds of a change, and the people running operations act on model output where they already work, without exporting a file or logging into a second system.

Common use cases

  • 01 Pull per-deployment TPM/RPM usage into a warehouse for FinOps chargeback and quota-exhaustion alerting.
  • 02 Mirror Assistants and Vector stores configuration into a database as an auditable inventory of retrieval assets and their linked files.
  • 03 Stream ship confirmations and tracking to the ERP and CRM so customer service answers from live data.
  • 04 Mirror inventory positions across the distribution network into a central warehouse for network-level visibility.

Common sync patterns

Continuous freshness, no reload

As records are created or corrected in Manhattan Associates WMS, the copy held in Azure OpenAI updates within seconds, so the model side never reasons over stale ERP data.

One consistent identity across both systems

A customer, supplier, item, or transaction in Manhattan Associates WMS maps to the corresponding entry in Azure OpenAI, so every AI result attaches to the right entity on both sides.

Document extraction round-trip

Purchase orders, invoices, or other documents flow from Manhattan Associates WMS into Azure OpenAI for parsing or classification, and the structured result returns to the record it came from.

What you can sync between Azure OpenAI and Manhattan Associates WMS

Representative objects on each side — any object or custom field can map to any target. Schemas are auto-detected; types are converted between the two systems.

Azure OpenAI objects Manhattan Associates WMS objects How this pairing syncs
Assistants Persistent assistants (preview) with instructions, tools, and linked files; read as configuration inventory, not authored via sync. Returns Return receipts synced so upstream systems reflect disposition and restock. Assistants is specific to Azure OpenAI and Returns to Manhattan Associates WMS — each maps to any object or custom field on the other side.
Vector stores File collections (preview) backing file-search retrieval; read as metadata such as name, file counts, and status. Appointments Dock scheduling data coordinated with carriers and yard systems. Vector stores is specific to Azure OpenAI and Appointments to Manhattan Associates WMS — each maps to any object or custom field on the other side.
Deployments Named model deployments (model, version, SKU, assigned TPM capacity) read as a control-plane inventory via Azure Resource Manager; read-only in sync. Distribution Orders Outbound order releases sent from the ERP or OMS into the WMS for waving and fulfillment. Deployments is specific to Azure OpenAI and Distribution Orders to Manhattan Associates WMS — each maps to any object or custom field on the other side.
Models Catalog of base and fine-tunable models available per region; read-only reference data used to resolve deployment and fine-tuning targets. Purchase Orders & ASNs Inbound expectations synced from procurement to drive receiving and putaway. Models is specific to Azure OpenAI and Purchase Orders & ASNs to Manhattan Associates WMS — each maps to any object or custom field on the other side.
Fine-tuning jobs Training jobs with status, base model, hyperparameters, and result files; status is polled from queued through succeeded or failed. Inventory On-hand, allocated, and available positions read out across the DC network. Fine-tuning jobs is specific to Azure OpenAI and Inventory to Manhattan Associates WMS — each maps to any object or custom field on the other side.
Files Uploaded training, validation, and batch-input files plus generated output files; listed and read by ID, not written back in sync. Items Item master and packaging data kept consistent with the ERP catalog. Files is specific to Azure OpenAI and Items to Manhattan Associates WMS — each maps to any object or custom field on the other side.

How changes propagate between Azure OpenAI and Manhattan Associates WMS

Each direction of the sync is driven by what the source system can signal and what the destination accepts — detection, delivery, and expected latency below.

Azure OpenAI Manhattan Associates WMS Interval-based propagation

DetectionStacksync polls Azure OpenAI for changes on an incremental schedule, reading only records changed since the previous pass. Polling: list endpoints plus GET on job IDs for status.

DeliveryEach detected change is written to Manhattan Associates WMS through its API, with automatic retries and rate-limit backoff.

Manhattan Associates WMS Azure OpenAI Interval-based propagation

DetectionStacksync polls Manhattan Associates WMS for changes on an incremental schedule, reading only records changed since the previous pass. Polling on modified timestamps.

DeliveryAzure OpenAI does not accept inbound record writes, so this direction carries requests rather than records: Azure OpenAI's output flows back as field updates on the originating Manhattan Associates WMS records.

Rate-limit considerations

  • Azure OpenAI: Per-deployment TPM and RPM limits (about 6 RPM per 1000 TPM), scoped by region and subscription; control-plane ARM calls throttle separately.
What ships with Azure OpenAI ⇄ Manhattan Associates WMS

Connect Azure OpenAI and Manhattan Associates WMS for flexible, real-time data sync.

Real-time sync, workflow automation, event queues, EDI, and monitoring, for every Azure OpenAI–Manhattan Associates WMS connection.

Real-time

Real-time sync

Changes in Azure OpenAI or Manhattan Associates WMS instantly reflect in both systems. No stale data, no manual imports.

No-code + pro-code

Workflow automation

Trigger automated workflows whenever Azure OpenAI or Manhattan Associates WMS data changes, update records, fire webhooks, or kick off sequences without brittle API scripts.

At scale

Event queues

Handle millions of events per minute without losing a single Azure OpenAI or Manhattan Associates WMS record.

Observability

Monitoring

Track your Azure OpenAI ⇄ Manhattan Associates WMS sync health, view errors, and replay failed events in one click.

Trading partners

EDI

Transform legacy EDI complexity into simple database interactions between Azure OpenAI and Manhattan Associates WMS.

How the Azure OpenAI and Manhattan Associates WMS connectors work

Azure OpenAI

Integration surface
REST data-plane (inference + authoring) and Azure Resource Manager control-plane
Authentication
API key in the api-key header, or a Microsoft Entra ID bearer token / managed identity
Change detection
Polling: list endpoints plus GET on job IDs for status; no webhooks or change feed. Fine-tuning and batch jobs expose queued/running/succeeded states.
Capabilities
read
Rate limits
Per-deployment TPM and RPM limits (about 6 RPM per 1000 TPM), scoped by region and subscription; control-plane ARM calls throttle separately.

Manhattan Associates WMS

Integration surface
REST API on Manhattan Active WM; database tables and message-based interfaces on legacy on-premises versions
Authentication
OAuth 2.0 on Manhattan Active; database credentials on legacy deployments
Change detection
Polling on modified timestamps; Manhattan Active also supports event/message-driven integration patterns
Capabilities
read · write
How it works

How to connect Azure OpenAI to Manhattan Associates WMS — three steps, no code

Configure and sync within minutes, no code. Whether you sync 50k or 100M+ records, Stacksync handles the queues, infra, and plumbing. Integrations are non-invasive and need zero setup on your systems.

  1. 01

    Connect your apps

    Authenticate Azure OpenAI and Manhattan Associates WMS with each platform's native method — OAuth, API keys, or service accounts — plus secure options like SSH tunneling, IP whitelisting, and VPC peering.

    • OAuth 2.0
    • SSH tunnel
    • VPC peering
    Azure OpenAI connected
    Manhattan Associates WMS connected
    OAuth 2.0
    SSH tunnel
    SSL certificate
    VPC peering
  2. 02

    Choose tables

    Pick the Azure OpenAI and Manhattan Associates WMS objects to sync — Stacksync auto-detects both schemas, including custom fields where the platform exposes them. Sync to existing tables, or let Stacksync create new ones with ideal data types.

    • Standard objects
    • Custom objects
    • Auto-schema
    objects · Azure OpenAI ⇄ Manhattan Associates WMS
    Customers 12,480
    Sales Orders 8,213
    Invoices 5,902
    Items 1,344
  3. 03

    Map fields

    Fields map automatically even when names and types differ. Stacksync handles transformation and type casting for you, zero configuration required.

    • Auto-map
    • Type casting
    • Transforms
    Azure OpenAI Manhattan Associates WMS
    Company company_name text
    Email email text
    Amount amount numeric
    Created created_at timestamp
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FAQ

Azure OpenAI and Manhattan Associates WMS integration FAQ

SECURITY

Security teams trust Stacksync

As a data company, we understand the importance of keeping your data secure. Stacksync is built with security best practices to keep your data safe at every layer, and is DPF-certified for US, EU, UK and CH data transfers.

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SSO & SCIM

Let your users access Stacksync from your centralized user management systems. Works with Okta, Azure, Google SSO and more.

Alerts

Immediately get alerted about record syncing issues over email, Slack, PagerDuty and WhatsApp. Resolve issues from a centralized dashboard with retry and revert options.

Secure connection options

Securely connects to your systems with:

OAuth 2 SSH Tunnelling SSL certificates IP Whitelisting VPN gateway VPC peering and more

Related integrations

Every pair below is a real-time, two-way sync. Search all 380 integrations available for Azure OpenAI and Manhattan Associates WMS.

Popular · 6 of 380
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