Global Shop Solutions + private AI
AI for Global Shop Solutions, Built for How Job Shops Actually Run
Short answer
AI on Global Shop Solutions (GSS) works by connecting to the platform's API and report data on top of its SQL Server database to ground a private language model on your quotes, scheduling, and job cost history, so a job shop owner can ask plain-language questions instead of pulling another Crystal Reports export. Because most GSS shops already run the system on their own server, the AI layer can usually sit on the same on-premise infrastructure, keeping quoting and customer data inside the shop rather than sending it to a public model API.
- ERP
- Global Shop Solutions ERP
- Industries
- Job Shop Manufacturing, Make-to-Order, Metal Fabrication
- Written for
- Owner
Global Shop Solutions built its reputation on being the ERP a job shop owner could actually run without a dedicated IT department: scheduling, quoting, shop floor data collection, and job costing in one system, usually on a server sitting in the shop's own server room. That practicality is exactly why AI has to be approached carefully here, an owner who chose GSS in part to avoid depending on outside infrastructure is not going to want their quoting history and customer list flowing through a third-party cloud AI service.
The day-to-day reality for a job shop owner is that most of the valuable knowledge in the business is not written down anywhere, it is in an estimator's head, built up from years of quoting similar work, or in a scheduler's intuition about which jobs are actually going to run late. GSS captures a lot of the underlying data, quote history, actual job costs, scheduling performance, but turning that data into a decision still depends on someone experienced enough to interpret it.
A private AI layer grounded in GSS data changes that by letting the owner or a newer estimator ask directly: 'what did we actually charge and what did it cost us the last three times we quoted a part like this' returns an answer grounded in real GSS job history, not a guess. A scheduler can ask 'which jobs are at risk of missing their ship date this week' and get an answer that traces current shop floor status against the schedule, instead of walking the floor to check manually.
Because most Global Shop Solutions installs run on a server the shop already owns, the practical deployment answer for most GSS customers is straightforward: put the AI model and its data extract on the same on-premise infrastructure, or a small dedicated server beside it, so customer quotes, pricing, and job cost data never leave the building. That matters as much for competitive reasons, protecting pricing knowledge from ending up in a shared model, as it does for any formal compliance requirement.
What usually gets in the way
The problems we hear most from owner teams running Global Shop Solutions ERP.
Quoting depends on one or two experienced estimators
Accurate quoting for one-off and low-volume work relies on an estimator's memory of similar past jobs, which does not transfer easily to a newer hire or survive when that estimator retires.
Schedule risk is spotted by walking the floor, not by the system
Knowing which jobs are actually at risk of missing a ship date usually comes from a scheduler's gut sense and a physical walk of the shop rather than a system-generated alert.
Job cost history is captured but rarely analyzed
GSS records detailed job cost data, but with no dedicated analyst on staff, that history mostly sits unused instead of informing future quotes and pricing decisions.
Crystal Reports and canned reports do not answer today's question
Building a custom Crystal Reports report for a one-off question takes longer than most owners are willing to spend, so ad hoc questions get answered from memory or a rough spreadsheet.
Customer and pricing data is sensitive competitive information
A job shop's quote history and pricing structure are exactly the kind of data an owner does not want flowing through a shared cloud AI service where it might inform a competitor's model indirectly.
Where AI earns its place in Global Shop Solutions ERP
Each use case names the ERP objects it reads or writes, so your ERP team can judge the integration effort before anyone commits budget.
Quoting assistant grounded in job history
When a new RFQ comes in, the assistant retrieves actual costs and cycle times from similar prior jobs, matched by part characteristics and process, to suggest a starting quote for the estimator to review.
Touches: GSS Quotes, historical Job Cost records, Routing and process data
Outcome: Gives newer estimators a data-grounded starting point instead of relying entirely on a senior estimator's memory.
Schedule risk explanation
The assistant reviews current job status against the schedule and flags which jobs are actually at risk of missing their ship date, explaining why based on operation completion and queue times.
Touches: GSS Scheduling, Shop Floor Data Collection, Work Order status
Outcome: Surfaces at-risk jobs from the system directly instead of relying on a manual floor walk to catch them.
Job cost and margin explanation
An owner asks why a specific job made or lost money and the assistant pulls labor, material, and overhead transactions against the original quote to explain the variance.
Touches: GSS Job Costing, Labor Tickets, Material Issues, original Quote estimate
Outcome: Turns a manual cost reconciliation into a same-day answer instead of a task that waits for slower periods.
Natural-language production status queries
Office staff or the owner ask 'where is job 8842' or 'what is due out this week' and get an answer grounded in current GSS scheduling and shop floor data.
Touches: GSS Work Orders, Scheduling, Shop Floor Data Collection
Outcome: Cuts interruptions to the scheduler for status questions the system already has the answer to.
Pricing and margin pattern review
Periodically, the assistant reviews closed jobs for pricing patterns, such as consistently underpriced part families or customers, and flags them for the owner to review at the next pricing update.
Touches: GSS closed Job Cost history, Customer and part-level pricing
Outcome: Surfaces systematic underpricing before it accumulates across many jobs, rather than after a bad year.
New estimator onboarding assistant
A newer estimator asks the assistant how similar jobs were historically quoted and priced, grounded in the shop's own GSS data rather than generic industry rules of thumb.
Touches: GSS historical Quotes, Job Cost records, part family data
Outcome: Shortens the time it takes a new estimator to quote at a level close to an experienced one.
Purchasing and material lead time assistant
The assistant flags material shortages against upcoming scheduled jobs and explains the lead time risk based on GSS purchasing and vendor history.
Touches: GSS Purchase Orders, Inventory, Vendor lead time history
Outcome: Gives the buyer earlier warning of material risk tied to specific upcoming jobs, not just a generic low-stock flag.
Reference architecture
The architecture connects to Global Shop Solutions through its API and reporting data on top of its SQL Server database, with the model and extracted data typically kept on the same on-premise infrastructure most GSS shops already run, avoiding any new dependency on outside cloud services for sensitive quoting and pricing data.
- 1
GSS connectors
Global Shop Solutions API and report data access for job cost, scheduling, quoting, and shop floor data, used for scheduled extracts and, where needed, more frequent status lookups.
- 2
Data and semantic layer
Extracted GSS data is organized into a semantic model mapping quotes, job cost transactions, and scheduling data into the business terms an owner and estimators actually use.
- 3
Model serving
An open-weight model served with vLLM or Ollama on a modest on-premise server sized for a single-shop deployment, avoiding recurring per-token costs to a public model API.
- 4
Retrieval and agents
Quoting, scheduling, and cost questions are answered through retrieval grounded in the GSS extract; any suggested quote or scheduling change routes back through GSS for a person to review and enter.
- 5
Governance and audit
Access mirrors GSS user permissions, and every question and generated answer is logged, which matters once the assistant is influencing quotes and pricing decisions.
Integration notes for your ERP team
- Global Shop Solutions exposes data through its API and standard report data structures on top of its SQL Server database; the AI layer should use these rather than querying the underlying database directly, to stay compatible across GSS updates.
- Job cost data in GSS ties labor tickets, material issues, and overhead to specific work orders and the original quote; the semantic layer needs those relationships mapped once so quoting and variance questions resolve consistently.
- GSS user permissions should be mirrored into the AI layer's access model so a shop floor user and an owner see answers scoped to what their own GSS login could already reach.
- For shops with heavy Crystal Reports customization, review existing reports before building new extraction logic, since they often already encode the business logic the semantic layer needs.
- Any suggested quote, scheduling change, or purchase action should go back through GSS for a named person to review and enter, rather than the AI layer writing directly to the database.
- For a single-shop, on-premise deployment, extract frequency can be kept simple, near-real-time for scheduling and status questions, and daily for quoting history and job cost analysis.
Deployment options
On-site, beside the shop's own GSS server
The majority of Global Shop Solutions shops, which already run GSS on their own on-premise SQL Server
The AI model and extracted data run on the same server or a small dedicated box in the same server room, so quoting and pricing data never leaves the building.
Private cloud for multi-shop groups
Owners running more than one shop or facility on GSS
A private cloud tenant aggregates data across shops for cross-facility quoting and scheduling visibility while keeping the model and data out of any public or shared AI service.
Hybrid for shops planning a future GSS cloud move
Shops considering GSS's cloud-hosted option down the line
The AI layer is built to extract via the API regardless of whether GSS itself later moves to cloud hosting, so the AI investment does not need to be redone if the shop's own hosting choice changes.
Compliance and data control
How the architecture supports your obligations. Certification and accountability stay with your organisation; the design keeps the evidence straightforward.
ITAR (for shops doing defense-related work)
Job shops machining parts under ITAR-controlled drawings keep any referenced technical data inside their own on-premise AI environment, never passed to a public model API.
AS9100 (for aerospace-supplying shops)
The AI layer is additive to existing traceability records: every quoting or job cost explanation links back to the source GSS transactions rather than replacing the documentation an AS9100 audit would review.
State data breach notification laws
Customer and pricing data extracted for AI grounding stays on infrastructure the shop controls, minimizing the exposure surface compared to sending exports to an outside service.
CMMC 2.0 (for DoD-supplying shops)
Shops holding DoD subcontracts through GSS keep controlled unclassified information referenced in job records inside their private, on-premise AI environment rather than a shared cloud service.
Where Netray fits
ERPray
The grounded question-answering pattern fits an owner's day-to-day quoting and job status questions well; a GSS connector is built as part of the engagement since it is not one of ERPray's out-of-the-box integrations today.
Custom build
Quoting assistants tuned to a shop's own part families and pricing history, and schedule risk detection, are typically bespoke builds rather than a generic template.
How an engagement runs
Phase 1 . 2 weeks
Discovery
- -Review of GSS server setup, existing Crystal Reports, and available API access
- -Priority use case selection with the owner
- -Draft semantic model for quoting, job cost, and scheduling data
- -Confirmation that the AI layer will run on-premise beside the existing GSS server
Phase 2 . 5-7 weeks
Pilot
- -GSS API integration for the selected data set
- -Private model deployed on-premise
- -One or two use cases live, e.g. quoting assistant and schedule risk explanation
- -Access controls mapped to existing GSS permissions
Phase 3 . 6-8 weeks
Production
- -Expansion to job cost explanation and pricing pattern review
- -Query and access logging reviewed by the owner
- -Training for estimators, schedulers, and office staff
- -Handover runbook for ongoing operation
Phase 4 . Ongoing
Scale
- -Rollout to additional shops for owners running more than one facility
- -Periodic review of quoting accuracy against actual job outcomes
- -Model and prompt updates as part families or pricing structure change
- -Right-sizing of the on-premise server as usage grows
Questions to ask any vendor, including us
A short list that separates real Global Shop Solutions ERP AI work from a chatbot demo.
- Will the AI model and our quoting and pricing data run entirely on our own server, or does any of it leave the shop?
- Can the assistant show the underlying GSS job records behind a quote or cost explanation, so we can verify it before trusting it?
- How does the tool's access control map to our existing GSS user permissions?
- Does any AI-suggested quote or scheduling change require a person to review and enter it, or could it write directly to GSS?
- How is the GSS connector maintained as we update our GSS version?
- What happens to our extracted data and semantic model if we end the engagement?
- Has the vendor looked at our existing Crystal Reports, or is the semantic model being built from scratch?
- What is the realistic hardware cost for a shop our size to host this on-premise?
Frequently asked questions
Can AI be added to Global Shop Solutions without sending our quoting data to a public AI provider?
Yes. GSS's API and report data structures support extracting quoting, job cost, and scheduling data into infrastructure the shop controls, typically the same on-premise server GSS already runs on. A privately hosted model answers questions grounded in that data without it reaching a public model API.
Do we need new hardware to run this?
Often a modest dedicated server or an upgrade to existing hardware is enough for a single-shop deployment, since the model size and query volume for a job shop's use cases are much smaller than an enterprise-scale deployment. Sizing is confirmed during discovery based on actual data volume and expected usage.
Can the assistant quote jobs automatically?
It can draft a starting quote grounded in historical job costs for similar work, which an estimator reviews and adjusts before it goes to the customer. The recommended pattern is the assistant proposes and a person approves, not the assistant issuing quotes directly.
How does this protect our pricing from competitors?
Because the model and the extracted job cost and quoting data stay on the shop's own on-premise infrastructure, none of that competitively sensitive data is sent to or processed by a shared or public AI service where it could indirectly influence a model used by other shops.
Will this replace Crystal Reports or our existing GSS reports?
No, it complements them. Existing reports remain the right tool for recurring, fixed-format output. The AI layer is better suited to ad hoc questions and quoting or scheduling decisions that would otherwise depend on an experienced person's memory.
Is this a Global Shop Solutions product?
No, it is a privately hosted AI layer built using GSS's existing API and report data, independent of Global Shop Solutions' own product roadmap. It gives a shop AI grounded on its own GSS data, on infrastructure the owner controls.
How long does a pilot take for a single job shop?
A focused pilot on one or two use cases, such as a quoting assistant and schedule risk explanation, typically takes five to seven weeks from kickoff to a working assistant that estimators and the scheduler are actually using.
Related guides
AI for ECI JobBOSS2 and E2 Shop System
Add AI to ECI JobBOSS2 or E2 Shop System for faster quoting, tighter scheduling, and shop-floor answers, without sending your job data to the cloud.
ProShop + on-prem AIAI for ProShop ERP in AS9100 Machine Shops
Add AI on top of ProShop ERP to speed up NCR/CAPA drafting, FAI review, and document control, without moving AS9100 quality records off your network.
RFQ-to-quote + on-prem AIAI for quote-to-cash in manufacturing and job shop ERPs
AI reads incoming RFQs, drafts quotes from your ERP's own cost and pricing history, and flags parts your shop has never quoted before, keeping the estimator in control.
AS9100D + on-prem AIAI for AS9100 Quality Management on Your ERP
AI on top of your ERP quality module for AS9100D suppliers: NCR/CAPA drafting, FAI support, counterfeit parts screening, with a full audit trail.
ERP AI Buyer GuideHow to Choose an ERP AI Implementation Partner
A CIO checklist for picking an ERP AI implementation partner: the architecture questions to ask, red flags, pricing models, and what to demand in the SOW.
QAD + on-prem AIAI for QAD Adaptive ERP in automotive and industrial manufacturing
Add AI to QAD Adaptive ERP or Enterprise Edition for automotive and industrial manufacturing, grounded on QXtend and QAD's API layer, on-prem or private cloud.
Plan it with numbers
Job Shop ERP Fit Assessment
Score how well your ERP handles high-mix, low-volume and make-to-order manufacturing across quoting, one-off routings, WIP visibility, subcontract tracking, and job-level cost variance.
Free ToolERP Implementation Timeline Estimator
Estimate a realistic ERP implementation duration from your user count, sites, module scope, customization level, and team availability, with contingency included.
Free ToolERP AI Copilot ROI Calculator
Turn user count, query volume, and time saved per question into a monthly savings, license cost offset, and payback period for an ERP AI copilot.
GuideEngineer-to-Order ERP Challenges and Solutions
Solve ERP challenges unique to engineer-to-order manufacturing. Project costing, dynamic BOMs, engineering integration, and progress billing solutions.
GuideYour First AI Agent: A Manufacturing Playbook
Your first AI agent in manufacturing: pick a bounded ERP use case, scope data access, set guardrails, and ship a working pilot in 6 to 8 weeks.
GuideSelecting an AI Implementation Partner: Evaluation Criteria
Selecting an AI implementation partner: a weighted evaluation scorecard, reference-check questions, and why a pilot-first contract beats a big-bang one.
Talk it through with an engineer who knows Global Shop Solutions ERP
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.