# AI for Aras Innovator That Understands Your ItemTypes and Honors Your Permissions

> Source: https://netray.co/erp-ai/aras-innovator-ai-integration
> Publisher: Netray (netray.co), AI and integration engineering for ERP and manufacturing systems.
> Section: AI for ERP

## Short answer

AI for Aras Innovator connects a private language model to the platform's OData REST endpoint and ItemType model so engineers can ask about parts, BOMs, ECOs and documents in plain English. Each request runs as the asking user so Aras permissions apply, answers cite the item and generation, and the AI stack can sit on-prem or in your own cloud.

## Kind

platform

## Erp Family

mfg-systems

## Erp Products

- Aras Innovator

## Industries

- Aerospace
- Defense
- Automotive
- Industrial Equipment
- Energy

## Region

Global

## Persona

PLM Architect

## Eyebrow

Aras Innovator + private AI

## Intro

Aras Innovator is a model-driven platform: nearly everything is an ItemType with properties, relationships, lifecycle maps, workflows and permissions that administrators define. Part, Part BOM, Document, CAD, and change items like ECR, ECO and ECN are provided out of the box and then extended heavily. Two companies running Aras can have very different data models.

That openness is the reason companies choose it, and it creates a particular problem for question answering. A generic text-to-query tool does not know your custom ItemTypes, your property names or your relationship semantics. Without that knowledge, it produces plausible but wrong answers, which is worse than none in an engineering system.

Aras also has a strong security model, with identities, permissions and access on each item. Any AI layer that reads data outside it, such as through a direct SQL extract used to answer user questions, undermines the control that regulated customers depend on.

This page lays out how to build AI on Aras in a way that respects the model: configuring the AI layer from your ItemType definitions, using the supported interfaces, keeping inference private, and being clear about when built-in search and reports are sufficient.

## Pain Points

### Deep customization hides the data model

**Detail:** Custom ItemTypes, properties and relationships mean newcomers cannot write queries without architect help, and generic tools mis-handle your schema.

### ECO preparation is manual

**Detail:** Preparing an ECO means gathering affected items, where-used, related documents and downstream impacts across several forms and grids.

### Search is exact, not conversational

**Detail:** Searching by property values works if you know them. Questions such as which released parts were changed for the same failure mode last year do not map to a search grid.

### Direct SQL access is tempting and risky

**Detail:** Reporting teams query the SQL Server database directly, which bypasses Aras permissions and can expose restricted items.

### Upgrades break custom integrations

**Detail:** Methods and integrations built against a specific version need retesting at each upgrade, so teams delay and fall behind.

## Use Cases

### ECO and ECN impact summaries

**Description:** From a change item, gather affected parts, where-used parents, related documents and open changes, then draft a plain-language impact statement with cited items.

**Erp Objects:** ECO or ECN ItemTypes, affected item relationships, Part, Part BOM, Document

**Outcome:** Review boards receive structured packages; analysts check rather than assemble.

### Conversational BOM queries

**Description:** Ask where-used, indented BOM, revision differences or which assemblies include a given supplier's component.

**Erp Objects:** Part, Part BOM relationship, generation and state properties

**Outcome:** Common structure questions are answered without a custom report.

### Schema-aware natural-language search

**Description:** Translate a question into an Aras search over your real ItemTypes and properties, show the criteria used, and return results with links.

**Erp Objects:** ItemType metadata, property definitions, saved searches

**Outcome:** Non-expert users can search without knowing the property names, with transparent criteria.

### Document and requirement retrieval

**Description:** Answer from specifications, test results and requirements stored as documents or requirement items, citing the item and generation.

**Erp Objects:** Document, file items, requirement ItemTypes where used

**Outcome:** Faster retrieval during design reviews and audits.

### Lifecycle and workflow monitoring

**Description:** Summarize what is waiting on whom across workflows and identify items stuck in a state beyond your service targets.

**Erp Objects:** Workflow process and activity items, lifecycle states, assignments

**Outcome:** Bottlenecks in review and release become visible without manual chasing.

### PLM to ERP BOM comparison

**Description:** Compare released Aras structures with ERP manufacturing BOMs and report quantity, unit and effectivity mismatches.

**Erp Objects:** Part BOM, ERP BOM and item master, integration logs

**Outcome:** Handoff errors are found before production planning uses them.

### Method and configuration help for administrators

**Description:** Answer questions about how custom methods, lifecycle maps and permissions are configured, using exported configuration and documentation.

**Erp Objects:** Server methods, lifecycle maps, identities, permissions, exported AML packages

**Outcome:** Shorter onboarding for new administrators and less tribal knowledge.

## Architecture

**Overview:** An AI service reads configuration from Aras to learn your ItemTypes, then queries through the OData endpoint or IOM as the asking user. A private model composes answers from retrieved items. Any write is a draft item created for a person to review.

**Layers:**

#### Schema layer

**Detail:** Reads ItemType, property and relationship definitions to build a map of your model, refreshed after upgrades or configuration changes.

#### Access layer

**Detail:** Aras OData REST endpoint with OAuth-based authentication where available, or IOM-based service code, executed as the asking user so permissions are evaluated by Aras.

#### Retrieval layer

**Detail:** Structured queries over items and relationships plus a vector index for documents, with ItemType, id, generation and state kept for citation.

#### Model layer

**Detail:** Open-weight models served with vLLM or Ollama on customer hardware, with no public inference calls.

#### Governance layer

**Detail:** Per-user logs, evaluation questions drawn from your actual data, and prompts that require citations.

## Deployment Options

### On-premises

**Fit:** Defense and aerospace programs with self-hosted Aras

**Detail:** AI services and models run on a GPU server in the same zone as the Aras application server, with no external traffic.

### Private cloud

**Fit:** Customers hosting Aras in their own cloud account

**Detail:** Dedicated GPU instances in the same region and network, with customer-managed keys and private endpoints.

### Hybrid with vendor-hosted Aras

**Fit:** Teams on a managed Aras subscription

**Detail:** The AI service runs in your environment and calls the supported API for entitled items only; confirm terms and network paths with Aras or your hosting partner.

## Compliance

### Run retrieval and inference inside your controlled boundary, enforce user-level permissions through Aras, and log access. Eligibility of users remains with your export-control process.

**Framework:** ITAR and EAR

### Where CUI flows through the AI service, controls for access, audit and encryption are implemented and documented for your system security plan.

**Framework:** CMMC Level 2 and NIST SP 800-171

### Answers cite controlled versions. Draft items are not released until approved through Aras workflows.

**Framework:** AS9100 and ISO 9001 document control

### Change summaries can reference PPAP and control plan items in your model, with formal approvals kept in the process.

**Framework:** IATF 16949 and customer-specific requirements

## Integration Notes

- Read ItemType and property metadata to ground query generation, and cache it with a refresh after each configuration package import.
- Prefer the OData endpoint and IOM over direct SQL for question answering so permissions and access rules apply.
- Use Aras methods only where necessary; custom server methods raise upgrade and test costs.
- Handle generation and state explicitly, so answers state whether an item is current, released or superseded.
- Throttle relationship expansion to avoid heavy load on the application server during peak hours.
- Compare to ERP BOMs through the ERP's own interface to keep the comparison independent.

## Product Fit

### Custom build

**Why:** Schema-aware connectors and ECO agents have to be configured for your ItemTypes, so this is bespoke by nature.

### DataRay

**Why:** Suitable when Aras documents, file shares and ERP data must be searchable together in a private environment.

## Engagement

### Discovery

**Duration:** 2 weeks

**Deliverables:**

- ItemType and relationship map
- Question inventory
- Permissions and export-control review
- Interface decision

### Pilot build

**Duration:** 4 to 6 weeks

**Deliverables:**

- Schema-aware connector
- BOM and change retrieval
- Cited answers
- Evaluation set

### Workflow agents

**Duration:** 4 weeks

**Deliverables:**

- ECO impact drafts
- Workflow bottleneck report
- ERP comparison
- Search translation with visible criteria

### Hardening

**Duration:** 2 to 3 weeks

**Deliverables:**

- Permission regression tests
- Upgrade runbook
- Model update plan
- Training

## Buyer Questions

- How does the system learn our ItemTypes, and what happens when we change them?
- Do queries run as the asking user through Aras, or through a broad account?
- Which Aras version and interface are supported?
- Can I see the search criteria behind each answer?
- How are drafts created, and who approves them?
- What is the impact on the application server load?
- How do you validate accuracy on our own data?
- What would built-in search or reports already do well enough?

## Faq

### Why does Aras need a schema-aware approach?

**Answer:** Because ItemTypes and properties are defined by your administrators, a generic tool would guess names and relationships. Reading the actual definitions and showing the query criteria used makes answers checkable and reduces confident but wrong results.

### Can it use direct SQL for speed?

**Answer:** It can for indexing under controlled conditions, but not for answering user questions, since SQL bypasses Aras permissions. For user-facing queries, the supported API route keeps the security model in force.

### Does it work with Aras in the cloud?

**Answer:** Potentially. If the API is reachable, the AI service can run in your own environment and pull entitled data. Contract terms and network paths should be confirmed first, and the design differs from a fully on-prem setup.

### Will it modify released items?

**Answer:** No. It reads and drafts. Creating or releasing items is done by users through Aras workflows, with the usual signatures and audit trail.

### How does it cope with upgrades?

**Answer:** The schema map is refreshed after upgrades and the evaluation set is re-run. Because it avoids deep custom server methods, upgrade effort is usually lower, though API changes between versions still need testing.

### Is a GPU required?

**Answer:** For private inference at useful speed, yes. A pilot can run on one data-center GPU server. Smaller models on modest hardware are possible for narrow tasks, with the trade-off in answer quality and concurrency.

### Can it help admins too?

**Answer:** Yes, for explaining configuration from exported packages and documentation. This is lower risk than engineering data questions and a good first use case.

## Search terms

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