FactoryLogix + private AI
AI for Aegis FactoryLogix: Ask Your Electronics MES About Lots, Lines and Escapes
Short answer
AI for Aegis FactoryLogix connects a private language model to the MES database and its integration interfaces so process, quality and NPI engineers can ask plain-English questions about unit genealogy, material lots, line performance and nonconformances, with the underlying query shown. Joined to ERP data for orders, costs and suppliers, it runs on-prem or in a private cloud so traceability data for regulated electronics never leaves your boundary.
- ERP
- Aegis FactoryLogix
- Industries
- Electronics, Aerospace, Defense, Medical Devices
- Written for
- Manufacturing Engineering Manager
FactoryLogix is an electronics-focused MES that covers NPI and DFM review, work instructions, material management, line execution, quality and traceability on one data model. In a contract manufacturer or an aerospace and defense electronics plant, that database ends up holding the single most valuable record the company owns: which reel, which stencil, which reflow profile and which operator touched which serial number.
The problem is access. Engineers who need to answer a customer escape question, such as which units shipped with a suspect capacitor lot, usually open several screens, run a stored report, or ask whoever knows the schema. During a containment event that delay is expensive, and the answer still has to be reconciled against the ERP shipment and sales order records.
Aegis ships its own reporting and analytics, and for fixed, well-understood KPIs those are the right tool. The gap is the open-ended question that crosses MES and ERP, or crosses structured genealogy and unstructured documents such as customer specs, IPC workmanship criteria, deviation records and supplier certificates.
This page describes what a private AI layer over FactoryLogix looks like in practice: what it reads, how it is kept read-only, how it joins to the ERP, and where it is genuinely better than the built-in reports and where it is not.
What usually gets in the way
The problems we hear most from manufacturing engineering manager teams running Aegis FactoryLogix.
Containment queries take too long
A suspect component lot or a failed reflow profile triggers a where-used and where-shipped question across units, work orders and shipments. Doing it by hand across screens and spreadsheets can take hours when the customer expects an answer the same day.
MES and ERP disagree at the edges
Work order quantities, scrap counts and lot numbers in the MES drift from the ERP backflush and inventory transactions. Finding the gap means someone joining two systems manually, usually at month end.
NPI knowledge lives in documents
DFM findings, first article results, customer-specific workmanship requirements and deviation approvals sit in PDFs and attachments, so lessons from one program rarely reach the next quote or build.
Ad hoc reporting depends on a few people
Only a handful of engineers know where line-level defect data, rework history and operator qualifications are stored, so every unusual question becomes a request in their queue.
Sending traceability data to a public model is not an option
Serial-level genealogy for defense or medical programs is controlled data with customer contract restrictions, so cloud chatbots are blocked by policy before anyone evaluates them.
Where AI earns its place in Aegis FactoryLogix
Each use case names the ERP objects it reads or writes, so your ERP team can judge the integration effort before anyone commits budget.
Lot-level containment and where-used
An engineer asks which units and shipments consumed a named component lot or reel and gets the genealogy, current location and customer shipment, with the query displayed.
Touches: FactoryLogix material lots and reels, unit genealogy, work orders; ERP shipment lines and sales orders
Outcome: Containment questions that took hours of screen hopping become a reviewed query answered in minutes, still confirmed by a quality engineer before any customer notice.
First pass yield and defect Pareto by line
Ask for yield and top defect codes by line, shift, product family or stencil, and drill into repeat offenders without building a new report.
Touches: Inspection and test results (AOI, SPI, ICT, functional), defect codes, line and shift data
Outcome: Faster recognition of a recurring process issue, typically days earlier than waiting for the weekly quality review.
MES to ERP reconciliation
Compare completed quantities, scrap and consumed material in the MES against ERP receipts, backflush and inventory adjustments, and list the exceptions.
Touches: MES work order completions, ERP production orders, inventory transactions
Outcome: Month-end variance chasing becomes a standing exception list that planners and cost accountants review.
NPI and DFM knowledge search
Search prior DFM findings, first article reports and customer requirements to answer how a similar board was built, with citations to the source document.
Touches: NPI project records, attached documents, BOM and AVL data, ERP item master
Outcome: Quotes and build plans start from past evidence instead of memory; accuracy depends on how consistently documents were attached.
Material shortage and MSD exposure
List open work orders waiting on a short component, and flag reels approaching moisture exposure limits on the floor.
Touches: Material management records, MSD tracking, ERP purchase orders and supplier promise dates
Outcome: Planners see shortage and floor-life risk in one answer rather than two systems.
Operator and equipment qualification checks
Answer which operators are qualified for a process and which units were built by an operator whose certification later lapsed.
Touches: Operator qualification records, work instruction execution history
Outcome: Audit preparation for customer or AS9100 reviews is faster because the evidence is queryable.
Nonconformance trend summaries
Summarise open nonconformances by root cause and supplier, drafting a corrective action narrative for human review.
Touches: Nonconformance and corrective action records, supplier master, receiving inspection data
Outcome: Quality engineers spend their time on the investigation rather than on collecting and formatting records.
Reference architecture
A read-only AI layer sits beside FactoryLogix. It reads from a replica or reporting copy of the MES database and the supported integration interfaces, joins to ERP data through the ERP's own APIs, and serves a locally hosted open-weight model. Every answer carries the query and source records.
- 1
Data access
A read-only account on a reporting replica of the MES SQL database, plus the vendor-supported integration interfaces. No writes to production tables and no direct access to the live transactional instance during peak production.
- 2
Semantic layer
A curated map of genealogy, lot, work order, defect and operator tables into business terms such as unit, reel, line and shift, so the model queries named views rather than raw schema.
- 3
Retrieval
An index of attached documents, work instructions, customer specs and deviation records with source citations, access-controlled to match who can see which program.
- 4
Model serving
An open-weight model served with vLLM or Ollama on the customer's GPUs, sized to the question volume, with no outbound calls to public model APIs.
- 5
Controls
Role-aware access, query logging, the generated SQL shown to the user, and human approval on any proposed action such as a hold or a corrective action draft.
Integration notes for your ERP team
- Prefer a read replica or reporting database for analytical queries so ad hoc questions never compete with line execution.
- Use the vendor's documented integration interfaces for anything beyond reads, and confirm supported versions with the vendor before relying on them.
- Map genealogy by unit serial, panel, reel and lot identifiers once, then reuse those views across all questions.
- Join to the ERP on work order, item and lot keys using the ERP's APIs rather than its database where an API exists.
- Where lines publish IPC-CFX messages, those events can feed machine-level questions, but only if they are already captured.
- Index document attachments with program-level permissions so a question from one customer's team cannot surface another customer's records.
- Log every generated query and the user who asked, and review the log in the first weeks to tune the semantic layer.
Deployment options
On-prem next to the MES
Defense, aerospace and medical electronics with customer data restrictions
GPU server in the plant or data center network that already hosts FactoryLogix, with no internet egress from the AI tier.
Private or sovereign cloud
Multi-site EMS providers consolidating several plants
Dedicated tenancy in a region of your choosing, connected to each site by private links, with per-site data scoping.
Air-gapped
Classified or export-controlled programs
Fully disconnected deployment with models, indexes and updates moved by approved media under your change process.
Compliance and data control
How the architecture supports your obligations. Certification and accountability stay with your organisation; the design keeps the evidence straightforward.
ITAR / EAR
Keeping the model, index and logs inside a boundary the customer already controls lets their export program decide who has access. Netray does not hold registrations on the customer's behalf.
AS9100 and customer flow-downs
Answers are traceable to source records, and the query is retained, which supports evidence requests during audits.
IPC traceability expectations
The AI layer reads existing genealogy and does not alter it; the system of record remains FactoryLogix.
FDA 21 CFR 820 / QMSR and ISO 13485
For medical programs, the AI is a read-only retrieval aid. Decisions, dispositions and records stay with validated processes in the MES and QMS.
Data retention
Question logs and indexes follow the customer's retention schedule and can be purged with program closure.
Where Netray fits
How an engagement runs
Phase 1 . 1 to 2 weeks
Discovery
- -Top 20 questions engineers ask today
- -Data source and access inventory
- -Program-level confidentiality rules
Phase 2 . 2 to 4 weeks
Semantic model
- -Business-term views over genealogy and quality data
- -ERP join keys documented
- -Read-only access configured
Phase 3 . 3 to 5 weeks
Pilot
- -Working Q&A for one line or program
- -Answer accuracy test set
- -Query log review
Phase 4 . 3 to 6 weeks
Rollout
- -Additional programs and sites
- -Role and permission model
- -Runbook and monitoring
Questions to ask any vendor, including us
A short list that separates real Aegis FactoryLogix AI work from a chatbot demo.
- Does the AI read the live MES database, a replica, or only exported data?
- Can we see the generated query behind every numeric answer?
- How are customer-program permissions enforced in document retrieval?
- What happens to question logs when a program is closed?
- Which FactoryLogix versions and interfaces are supported for your approach?
- Who validates answers during a real containment event, and what is the fallback?
- Can the model run with no internet access at all?
- What would we still need to do in the native FactoryLogix reports?
Frequently asked questions
Does Aegis FactoryLogix already include AI?
Aegis offers analytics and reporting within the platform, and these are the right choice for fixed KPIs and standard dashboards. A separate private AI layer helps when the question is open-ended, spans MES and ERP, or must search documents together with structured genealogy. Check the current Aegis roadmap and licensing before deciding what to add.
Can an AI model change FactoryLogix records?
Not in the default design. The AI layer reads a replica or reporting copy and returns answers with queries shown. Any write-back, such as placing a hold, would be a separate, explicit workflow with human approval and would use the vendor's supported interfaces rather than direct database changes.
Is serial-level traceability data safe with a language model?
It depends where the model runs. With an open-weight model hosted on your own hardware and no outbound calls, the data stays inside your boundary. Public chatbot services are a different risk and are usually blocked by customer contracts for regulated programs.
How accurate are answers about lot genealogy?
Accuracy comes from the semantic layer and the displayed query, not from the model's memory. We test against a set of known questions with verified answers before pilot, and engineers can inspect the query. For customer-facing containment notices, a quality engineer should always confirm the result.
Does it work for contract manufacturers with many customers?
Yes, provided program-level permissions are modelled. Structured queries can be scoped by customer or program, and document indexes are filtered by the same rules, so one customer's team does not see another's data. This scoping is the most important design decision for an EMS provider.
Do we need a GPU server?
For interactive use by engineers, a modest GPU server running a mid-sized open-weight model is typically enough, and we size it from your concurrent users and document volume. Some sites start on a shared GPU in an existing data center before buying dedicated hardware.
How is this different from exporting data to Power BI?
Power BI is excellent for recurring, designed dashboards. The AI layer handles the unplanned question without a report build, and can combine documents with data. Many sites keep both: dashboards for routine KPIs and AI for investigation.
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Talk it through with an engineer who knows Aegis FactoryLogix
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.