EpicorERP Platform

Kinetic + private AI

AI for Epicor Kinetic, Beyond What Prism Covers

Short answer

Epicor Kinetic already exposes a rich data model through BAQs (Business Activity Queries), BPM (Business Process Management) directives, and the REST v2 API, and Epicor's own Prism AI features cover a growing set of embedded scenarios. A private LLM grounded in that same BAQ and REST layer extends coverage to ERP manager-specific questions, cross-module agents, and on-prem deployment where Prism's cloud dependency is not an option.

ERP: Epicor Kinetic, Epicor ERP 10
Industries: Discrete Manufacturing, Electronics, Industrial Equipment
Written for: ERP Manager

Discuss your Epicor Kinetic environment See use cases

Epicor Kinetic (the current name for Epicor ERP 10.2+) gives an ERP manager more structured access to data than most legacy ERPs: BAQs let you build reusable queries without writing SQL by hand, BPM directives let you hook business logic into transactions, and the REST v2 API exposes most of that programmatically. Epicor has also been shipping Prism, its own embedded AI layer, into more of the product. The real question for most Kinetic shops is not whether to use AI, but where Prism's coverage ends and a private, on-prem layer grounded in the same BAQ and REST data starts.

Prism's roadmap is cloud-first and Epicor-controlled: it works well for the scenarios Epicor has built for, on Epicor's schedule, with Epicor's data handling terms. For a Kinetic shop that is on-prem or in a private cloud, or that has cross-module questions Prism does not cover (blending BAQ results with a shop's own SOPs, drawings, and quality documentation, for instance), a private LLM run on customer infrastructure fills that gap without waiting on Epicor's product roadmap or sending data to Epicor's AI backend.

Kinetic's BPM engine already embodies years of accumulated business logic (custom validations, approval routing, data transformations) written by internal staff or an Epicor partner. Most ERP managers do not have a clean, current inventory of what every BPM directive does, and that gap becomes a real cost during an upgrade from a legacy Epicor 10.1 or Vantage environment, or when a directive starts misbehaving and nobody remembers why it was written.

The practical path for most Kinetic shops is additive, not either/or: keep using Prism where it already covers a scenario well, and add a private retrieval and agent layer over the same BAQ/REST data for on-prem deployment, cross-module questions, and the BPM documentation problem Prism does not solve.

What usually gets in the way

The problems we hear most from erp manager teams running Epicor Kinetic.

Prism doesn't run on-prem

Shops running Kinetic on-premises or in a private cloud for data control reasons cannot use Prism's cloud-dependent AI features the same way a SaaS Kinetic tenant can, leaving a real capability gap.

Nobody has a current BPM directive inventory

Years of accumulated BPM pre-processing, post-processing, and data directives exist with inconsistent documentation, making any Kinetic upgrade or troubleshooting exercise slower than it should be.

BAQ sprawl makes reporting requests slow

Every new question from operations or finance turns into a request for a new BAQ or BAQ report, when a natural-language layer over the existing BAQ catalog could answer many of them directly.

Cross-module questions fall outside Prism's scope

Questions that blend ERP data with shop-floor SOPs, quality documents, or engineering drawings are outside what an embedded, transaction-scoped AI feature like Prism is built to answer.

REST v2 API access exists but isn't leveraged for AI

Many Kinetic shops have already built REST v2 integrations for other purposes but have not connected that same access to a retrieval or agent layer that could reuse it.

Where AI earns its place in Epicor Kinetic

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

Natural-language BAQ assistant

Users ask plain-English questions about jobs, inventory, and orders, and the model either answers from an existing BAQ or composes a new query against the Kinetic data model, showing the underlying logic.

Touches: BAQ Designer definitions, JobHead, JobOper, PartTran, OrderHed via REST v2

Outcome: Cuts the time between a business question and an answer from a multi-day BAQ request to minutes for routine questions.

BPM directive documentation and explainer

An agent reads BPM directive definitions (conditions, actions, custom code) and produces a plain-language explanation of what each directive does and what would break without it.

Touches: BPM Pre-Processing, Post-Processing, and Data Directives; custom C# method directives

Outcome: Turns an undocumented BPM catalog into a reviewed reference, shortening upgrade and troubleshooting cycles.

Job status and exception shop-floor assistant

Operators and supervisors ask about job status, material shortages, or operation queue delays in plain language instead of navigating Kinetic screens.

Touches: JobOper, JobMtl, ResourceGroup, MES/shop floor data

Outcome: Reduces time spent hunting for status information during a shift, particularly for less experienced Kinetic users.

Engineering change impact assistant

When an ECO changes a BOM or routing, the model summarizes affected open jobs, quotes, and inventory using Kinetic's Engineering Workbench data, alongside related quality or drawing documents.

Touches: ECOGroup, PartRev, Method of Manufacture, MtlQueue

Outcome: Shortens the manual cross-check engineers currently do to find every order or job touched by a BOM revision.

Quote-to-cash drafting from job history

For a new RFQ, the model surfaces comparable historical jobs and their actual costs and lead times from Kinetic job costing data, drafting a starting quote for the estimator to refine.

Touches: Quote, QuoteDtl, JobHead actual cost and labor history

Outcome: Speeds up quoting for repeat-like work by giving estimators a grounded starting point instead of starting from a blank quote.

Purchase order and supplier follow-up

An agent drafts follow-up communications for overdue PO lines and supplier confirmations, using Kinetic purchasing data, with drafts reviewed before sending.

Touches: POHeader, PODetail, Vendor, PO acknowledgment status

Outcome: Reduces the manual follow-up workload for buyers on routine, low-risk purchase lines.

AS9100 or ISO quality documentation drafting

For manufacturers under AS9100 or ISO 9001, the model drafts NCR, CAPA, or 8D documentation from Kinetic quality module records and related inspection data, for the quality team to review.

Touches: NonConformance, CorrectiveAction, inspection results tied to JobOper

Outcome: Cuts the drafting time for quality documentation while keeping the quality engineer's sign-off as the final step.

Reference architecture

The model runs on customer-owned or private-cloud GPUs and reads Kinetic data through the existing BAQ layer and REST v2 API, without requiring new integration middleware. For on-prem Kinetic deployments, this closes the gap Prism's cloud dependency leaves open.

1
Kinetic connectors
REST v2 API access using Kinetic's existing authentication, plus direct read access to BAQ definitions so the model can reuse (or extend) queries the business already trusts.
2
Data and semantic layer
A mapping from Kinetic's BAQ and table naming to the business language planners, buyers, and quality staff actually use, refined against the customer's specific BPM customizations.
3
Model serving
An open-weight model served with vLLM or Ollama on customer GPU hardware, sized to Kinetic's transaction and user volume.
4
Retrieval and agents
Retrieval-augmented answers grounded in live BAQ and REST v2 data, plus draft-only agents for quoting, PO follow-up, and quality documentation that stop at a human review step.
5
Governance and audit
Every answer and draft is logged with its source BAQ or REST call; the system respects Kinetic's existing user security and company/site scoping.

Integration notes for your ERP team

Primary integration is via Kinetic's REST v2 API, using the same OAuth-based authentication and company/site scoping Kinetic already enforces for other integrations.
BAQ definitions are read directly to seed the semantic layer and to let the model reuse existing, business-approved queries rather than reconstructing logic from scratch.
BPM directive definitions (conditions, actions, and any custom C# method directives) are read for the documentation use case without altering directive execution.
Where a Kinetic shop already has Prism enabled, the private layer is additive; it does not require disabling Prism, and the two can be scoped to different use cases.
Any write-back (a PO acknowledgment update, a quality record) goes through the REST v2 API with the same validation and BPM logic Kinetic already applies, with a human approval step first.
Multi-company and multi-site Kinetic environments are respected; the model's data scope follows the same site and company boundaries the requesting user already has.

Deployment options

Air-gapped on-prem

ERP managers running Kinetic on-premises who cannot use Prism's cloud-dependent features and want AI fully inside their own network.

The model runs on GPU hardware alongside the on-prem Kinetic application server, connecting via REST v2 over the internal network.

Private or sovereign cloud

Shops running Kinetic in a private or hosted cloud who want AI capability without sending data to a third-party AI backend outside their control.

The model runs in a private VPC with a secured REST v2 connection to Kinetic, under the customer's own access and logging controls.

Hybrid

Kinetic SaaS customers who use Prism for its covered scenarios and want a private layer specifically for on-prem-sensitive data, cross-module questions, or BPM documentation.

The private layer runs alongside Prism, connecting to the same REST v2 API for the use cases Prism does not address, without disrupting existing Prism usage.

Compliance and data control

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

Data residency and access control

Kinetic data and AI query logs stay inside the customer's network or private cloud tenancy, and the AI layer inherits Kinetic's existing company, site, and user security rather than a separate permission model.

AS9100 / ISO 9001 traceability

AI-drafted quality documentation (NCR, CAPA, 8D) is logged with its source inspection and job data, and requires quality engineer sign-off before it becomes part of the official record.

Change management

The BPM directive explainer documents existing logic; it does not modify BPM directives, keeping any actual change under the customer's normal Kinetic change control process.

Vendor data handling

For shops that need to keep data out of any third-party AI backend, including Epicor's own Prism infrastructure, the on-prem or private-cloud deployment keeps all inference inside the customer's boundary.

Where Netray fits

ERPray

Natural-language question answering, dashboards, and agents grounded in Kinetic's BAQ and REST v2 data, read-only by default, with the underlying query visible.

Custom build

The BPM directive explainer and engineering change impact assistant are Kinetic-specific accelerators built around BPM and Engineering Workbench data.

How an engagement runs

Phase 1 . 2-3 weeks

Discovery

-Kinetic environment, BAQ catalog, and BPM directive inventory review
-Assessment of where Prism already covers a use case versus where it does not
-Priority use case selection and data access plan

Phase 2 . 6-8 weeks

Pilot

-Working retrieval layer over a defined BAQ/REST scope
-One agent workflow (quoting or PO follow-up) in draft-only mode
-Accuracy review against real user questions

Phase 3 . 4-6 weeks

Production

-Hardened deployment on customer GPU hardware or private cloud
-Access controls aligned to Kinetic company/site security
-Logging and audit trail in place

Phase 4 . Ongoing

Scale

-Additional use cases (BPM documentation, quality drafting)
-Expanded data and site scope
-Coordination with any Prism usage to avoid overlap

Questions to ask any vendor, including us

A short list that separates real Epicor Kinetic AI work from a chatbot demo.

For a scenario I want AI on, does Epicor Prism already cover it, or is this genuinely a gap?
Does this require any change to our Kinetic BAQ, BPM, or REST v2 configuration?
Where does the model run, and does any Kinetic data leave our network or go to a third-party AI backend?
How does the system handle our multi-company or multi-site security boundaries?
Can the vendor show how the BPM directive explainer arrives at its summary, not just claim accuracy?
Who reviews an AI-drafted quote, PO follow-up, or quality record before it is used?
What is the realistic GPU sizing and cost for our transaction and user volume?
Can we pilot on one BAQ area or module before expanding scope?

Frequently asked questions

Isn't Epicor Prism enough AI for Kinetic already?

For the scenarios Epicor has built into Prism, on a SaaS Kinetic tenant, it can be. The gap is on-prem or private-cloud deployments where Prism's cloud dependency does not apply, and cross-module questions that blend ERP data with documents Prism was not built to read.

Can AI work with Kinetic's BAQ system directly?

Yes. Existing BAQ definitions can be read to seed a natural-language layer, so the model reuses queries the business has already validated instead of reconstructing data logic independently.

How does this help with undocumented BPM directives?

An agent reads BPM directive conditions, actions, and any custom code, then produces a plain-language summary of what each directive does and its dependencies, turning an unmanaged catalog into a reviewed reference before an upgrade or troubleshooting exercise.

Is our Kinetic data safe if we add a private AI layer?

The model runs on customer-owned or private-cloud infrastructure, connects via Kinetic's existing REST v2 authentication and security scoping, and no Kinetic data needs to go to a third-party AI backend for the system to function.

Can this work alongside Prism rather than replacing it?

Yes. Most Kinetic shops that adopt a private layer keep using Prism for the scenarios it already covers well, and scope the private layer to on-prem needs, cross-module questions, or BPM documentation Prism does not address.

Does this require Epicor consulting hours to set up?

The integration uses Kinetic's standard REST v2 API and BAQ access, which most Kinetic shops already have enabled; Epicor partner involvement is not required for the AI layer itself, though your usual BPM or BAQ owner should be involved in scoping.

How long does a Kinetic AI pilot take?

A focused pilot on one area, such as job status questions or PO follow-up, typically takes 6-8 weeks to validate accuracy before expanding to additional BAQ areas or agent workflows.

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