Proficy + ERP + private AI
AI for GE Vernova Proficy: Historian, Plant Applications and ERP Questions Answered Privately
Short answer
AI for GE Vernova Proficy connects a private language model to Proficy Historian data and the Plant Applications database so engineers and production managers can ask about trends, downtime, quality events and production in plain English, with the query shown. Joined to ERP orders and lots, it runs on-prem, read-only and outside the control network.
- ERP
- GE Vernova Proficy, Proficy Historian, Proficy Plant Applications, Proficy iFIX
- Industries
- Manufacturing, Food and Beverage, Life Sciences, Energy
- Written for
- Operations Technology Manager
Proficy is the industrial software family that GE Digital built and that now sits under GE Vernova. For manufacturers it typically means Proficy Historian for time-series data, iFIX or CIMPLICITY for HMI and SCADA, Plant Applications for MES functions such as production events, downtime, genealogy and quality, and Operations Hub for dashboards.
Many Proficy sites are long-lived. Historians hold a decade or more of process data, Plant Applications models have been tuned through years of line changes, and the people who know the unit and event model are few. That history is valuable, but the investment in expertise to retrieve it is high.
The platform offers its own analytics and dashboards, and GE Vernova continues to develop the portfolio, so use the native tools for live monitoring and standard KPIs. A private AI layer is for unplanned questions, cross-system questions between the plant and the ERP, and plain-English access for people who do not know the data model.
This page describes how that layer attaches to Proficy data, how it stays strictly read-only and outside the control network, and the cases where the native tooling is the better choice.
What usually gets in the way
The problems we hear most from operations technology manager teams running GE Vernova Proficy.
Historian data is rich but hard to find
Tags are organised by collectors, areas and naming conventions that grew over years. Finding the right tag for a question needs a specialist, and mistakes in tag choice produce confident but wrong trends.
Plant Applications models are specialised
Production units, events, variables and specifications form a data model that is powerful but unfamiliar to most analysts, so reports depend on a few people.
Downtime and quality data is not joined to ERP orders
Events carry a production unit and time, while the ERP knows orders, lots and customers. Linking a quality deviation to the order and its materials is manual and slow.
Regulated records limit tool choice
In life sciences and food, electronic records and review workflows are validated. Teams reasonably resist any AI that might alter or bypass those records.
Multiple sites, multiple versions
Different plants run different Proficy versions and models, which blocks a single enterprise view without a common semantic layer.
Where AI earns its place in GE Vernova Proficy
Each use case names the ERP objects it reads or writes, so your ERP team can judge the integration effort before anyone commits budget.
Historian trend and comparison Q&A
Ask for a variable across a batch or time window and compare it with a golden batch or prior campaign, with the query visible.
Touches: Historian tag values and tag metadata; ERP batch or production order times
Outcome: Faster investigation of deviations; quality depends on a curated tag dictionary for the area.
Downtime analysis from Plant Applications
Rank downtime by unit, reason tree and shift, and explain the drivers using the reason hierarchy and event history.
Touches: Plant Applications downtime events, production units, reason trees, crews and shifts
Outcome: Targeted reliability work based on the largest recurring losses rather than the most recent incident.
Genealogy and lot trace
Trace where a raw material lot was used and which finished lots contain it, combining plant events with ERP lot records.
Touches: Plant Applications genealogy and events, ERP lot master, receipts and shipments
Outcome: Recall and complaint investigations start from a prepared trace that a quality lead then verifies.
Quality variable and specification review
Find samples outside specification, group by product and unit, and show the process context around them.
Touches: Plant Applications variables and specifications, test results, historian context
Outcome: Faster pattern recognition across products; confirmation requires process and quality engineers.
Production versus schedule
Compare actual production from Plant Applications with the ERP schedule and list orders at risk.
Touches: Production counts, ERP production orders, due dates
Outcome: Supervisors see at-risk orders earlier, with underlying numbers.
Alarm and event summary for shift handover
Summarise notable alarms, downtime events and open issues for the incoming supervisor to review.
Touches: iFIX or CIMPLICITY alarm history, event records, open orders
Outcome: Handover prep shortens and is less dependent on individual memory.
Documentation and configuration helper
Answer how a model, variable or calculation is configured from exported configuration and SOPs, with citations.
Touches: Plant Applications configuration exports, SOPs, specifications
Outcome: Knowledge becomes less dependent on one or two experts.
Reference architecture
The AI layer reads replicated Historian data and the Plant Applications SQL database through read-only accounts and curated views, calls the ERP's APIs for orders and lots, and uses a locally hosted model. It lives in an IT or DMZ zone with no route to the control network.
- 1
Data access
Read-only access to a replica of the Plant Applications database and to Historian data through supported query interfaces, kept off the production servers.
- 2
Tag and model dictionary
A curated dictionary of tags, production units, events and variables in business terms, built with the controls and MES engineers.
- 3
ERP join
Order, lot and product data from the ERP aligned by production unit and time window.
- 4
Model serving
An open-weight model on your GPUs; tool calls generate queries against defined views, and results return with the query and rows.
- 5
Controls
Read-only access, no writes to tags or PLCs, logged queries, role scoping by plant and unit, and approval for any proposed action.
Integration notes for your ERP team
- Confirm the Proficy product versions in each plant first, since Historian, Plant Applications and iFIX interfaces and schemas differ across releases.
- Use supported Historian query interfaces or a replicated SQL copy; avoid load on collectors and production servers.
- Read the Plant Applications SQL database from a replica through named views for events, units, variables and genealogy.
- Build a shared dictionary linking tag names, production units and ERP work centres so joins are deterministic.
- Use ERP APIs for orders, lots and products, matched to production unit and time window.
- Check time stamps and time zones across the Historian, Plant Applications and ERP before comparing events.
Deployment options
IT-zone on-prem
Most plants
GPU server in the plant IT or DMZ layer, reading replicated data through defined conduits.
Private cloud for multi-site
Enterprises with many Proficy sites
Replicated data from each site collected centrally with site-level permissions.
Air-gapped
Defense or critical infrastructure
Isolated analysis environment fed by approved transfers.
Compliance and data control
How the architecture supports your obligations. Certification and accountability stay with your organisation; the design keeps the evidence straightforward.
FDA 21 CFR Part 11 and GxP
The AI is a retrieval aid. Electronic records, signatures and review stay in validated Proficy and quality systems.
IEC 62443 and NIST SP 800-82
Placement in a higher zone with read-only conduits supports zone and conduit design; the customer owns the risk assessment.
FSMA and food safety programs
Lot trace preparation can be accelerated, with final trace results verified by food safety staff.
ITAR / EAR
Where process data is controlled, hosting inside the customer's boundary lets their export program govern access.
Where Netray fits
Custom build
A bespoke dictionary, views and retrieval layer is needed for your Proficy models and ERP.
DataRay
For asking questions across the Plant Applications database, Historian exports and documents on-prem, DataRay suits the multi-source pattern.
ERPray
ERPray covers the ERP side of the question for supported systems.
How an engagement runs
Phase 1 . 1 to 2 weeks
Discovery
- -Proficy version and architecture review
- -Zone and data flow assessment
- -Priority questions from operations and quality
Phase 2 . 3 to 5 weeks
Dictionary and views
- -Tag and model dictionary for pilot unit
- -Curated read-only views
- -Replica access configured
Phase 3 . 3 to 5 weeks
Pilot
- -Q&A for one area
- -Verified answer test set
- -Security and validation review
Phase 4 . 4 to 8 weeks
Rollout
- -More units and plants
- -Dictionary maintenance process
- -Monitoring and runbook
Questions to ask any vendor, including us
A short list that separates real GE Vernova Proficy AI work from a chatbot demo.
- Which Proficy products and versions are in use at each plant?
- Where does the AI sit relative to control network zones?
- How is the dictionary built and who maintains it?
- Does the design pass our validation approach for regulated records?
- Can every answer show its query and underlying rows?
- What load will analytical queries place on the Historian and databases?
- Which questions stay better served by native Proficy tools?
Frequently asked questions
Can AI query Proficy Historian data?
Yes, through supported query interfaces or a replicated copy. The reliable approach is a curated tag dictionary and defined queries, so the model chooses from known tags rather than guessing. Results show the query, tags and time range so engineers can check them.
Does GE Vernova offer AI for Proficy?
GE Vernova continues to develop analytics across the Proficy portfolio, so check current offerings and licensing for your version. A separate private layer helps with cross-system questions to the ERP, plain-English access, and when policy requires the model to run entirely on your own infrastructure.
Is it compliant for regulated pharma or food plants?
The AI layer is a read-only retrieval aid and does not create or alter electronic records. Validated systems remain the record, and your quality unit decides how AI outputs may be used in investigations. We document the design so it can be assessed against your validation approach.
Can it write back to Plant Applications or the ERP?
Not in the standard design. If you want proposed actions, such as drafting an event comment or a corrective action, a person approves them, and any write uses supported interfaces with an audit trail. Writes to control systems are out of scope.
How does it handle old Proficy versions?
Older versions can often be supported through a SQL replica and exports, but table structures and interfaces vary, so we confirm in discovery. Upgrading a Proficy site is a separate project and the AI layer does not require it.
What hardware is needed?
A single GPU server with a mid-sized open-weight model is often enough for engineering and production users in a plant. We size according to concurrent users, document volume and whether multiple sites share one deployment.
Will this replace Operations Hub dashboards?
No. Dashboards remain the right tool for live and recurring KPIs. The AI layer adds ad hoc investigation, summaries, and cross-system questions, using the same underlying data.
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Talk it through with an engineer who knows GE Vernova Proficy
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.