E2 Shop System + metal fabrication
AI for E2 Shop System in Metal Fabrication: Quote History, Material Yield and Delivery Risk
Short answer
AI for an E2 Shop System metal fabricator reads the shop's estimates, orders, jobs, material usage and shipping history so estimators and managers can ask questions like which sheet-metal jobs lost money to scrap or outside finishing. It runs read-only on your network, shows the query for every answer, and keeps customer drawings and pricing off public AI services.
- ERP
- E2 Shop System, ECI E2
- Industries
- Metal Fabrication, Sheet Metal, Welding
- Written for
- Operations Manager
Metal fabrication is a business of thin margins and volatile inputs. A shop running laser or turret cutting, press brakes, welding and powder coat on E2 Shop System is quoting steel, stainless and aluminum whose price moves between the quote and the purchase, then routing the job through several departments and often an outside finisher. Getting any one of those wrong can erase the margin.
E2 records a great deal that bears on this: the estimate with its material, labor and outside service lines, the job's actual material issues and time by operation, and the delivery history by customer. But the useful questions cross those records. Did the nest yield we assumed in the quote hold up? Which customers' welded assemblies run over on fit-up? Are powder coat vendors the reason jobs miss their dates?
Fabricators also hold drawings and designs that customers consider proprietary, and some supply defense or energy customers with strict handling rules. Uploading job histories to a public AI tool is a nonstarter for them, so the practical route is a model running on equipment the shop owns.
This page covers what private AI on E2 looks like in a fabrication shop: the records it uses, the use cases that tend to pay back first, where E2's own features and your nesting software already do the job, and what to ask before connecting anything to a live database.
What usually gets in the way
The problems we hear most from operations manager teams running E2 Shop System.
Material price moves between quote and purchase
Steel and stainless pricing and surcharges change quickly. Quotes built on last quarter's price per pound or sheet leave margin on the table, and it is hard to see after the fact which jobs were hit hardest.
Nest yield assumptions are rarely checked
Estimates assume a material utilization, but actual consumption depends on nesting, remnants and scrap. Comparing assumed to actual across hundreds of jobs is tedious, so the assumption stays wrong.
Multi-department routings hide bottlenecks
A job flows through cutting, forming, welding, finishing and shipping. Delays accumulate between departments, and the real constraint, often welding capacity or outside coating, is argued about rather than measured.
Outside services decide delivery dates
Powder coat, plating, galvanizing and heat treat vendors control the last leg of many jobs. Late returns are noticed when the customer calls.
Estimating is a specialist skill held by few people
Reading a drawing and turning it into material, cut time, bends, weld inches and finish is learned over years. When the senior estimator is busy, quotes slow down or get less careful.
Where AI earns its place in E2 Shop System
Each use case names the ERP objects it reads or writes, so your ERP team can judge the integration effort before anyone commits budget.
Quoted versus actual material yield
The assistant compares assumed material utilization and cost on estimates to the actual material issued to the job, by material type and customer, and highlights where the quoting assumption is consistently off.
Touches: Estimates, estimate material lines, job materials, inventory issues, purchase receipts
Outcome: Lets the shop correct yield assumptions with evidence, which tends to tighten quotes on high-volume sheet and plate work.
Similar-job estimate lookup
An estimator describes a new part by material, thickness, size, bends, weld length and finish and gets back comparable past estimates and the jobs they became, with actual hours by department.
Touches: Estimates, jobs, job operations, labor and time records, customers
Outcome: Speeds up quoting and gives less experienced estimators the history a senior estimator would draw on.
Department bottleneck view
The assistant measures queue time and completed operations by work center, and says in plain English which department held up the most jobs last month and on which part families.
Touches: Job operations, work centers, scheduled and completed dates, labor records
Outcome: Replaces opinion about the bottleneck with a measured answer to inform overtime, hiring or outsourcing decisions.
Outside finishing tracker
Open outside service purchase orders past promise date are listed with the affected jobs and customer due dates, with a drafted follow-up to the vendor for the buyer to approve.
Touches: Purchase orders, outside service operations, vendors, job due dates
Outcome: Moves outside-vendor chasing from a daily manual check to an exception queue.
Surcharge and price-change impact review
After a material price change, the assistant lists open quotes and orders whose margin is most exposed, based on the material cost recorded on each.
Touches: Open estimates, sales orders, material items, vendor price history, purchase orders
Outcome: Gives management a quick list of quotes to reprice or confirm before accepting orders at old prices.
Weld and fabrication job cost review
For a completed welded assembly, the assistant summarizes hours by operation against the estimate, rework and consumables, and flags where fit-up or fixturing time ran long.
Touches: Job cost, labor tickets, job notes, rework or NCR records, material issues
Outcome: Makes post-job reviews quick enough to actually happen and feeds lessons back into the next estimate.
Customer delivery and order status answers
Customer service asks where an order is, what is left to do and when it will ship, and gets an answer from live job status rather than calling the floor.
Touches: Sales orders, jobs, completed operations, shipping records
Outcome: Reduces interruptions to shop supervisors and gives customers consistent answers.
Reference architecture
The AI layer reads E2 data through a read-only database account or scheduled export on your network, maps it to fabrication terms, and returns answers with the underlying query visible. Anything that would change a record is a draft for a person to approve.
- 1
E2 data access
Read-only access to the E2 database, typically Microsoft SQL Server in current versions, or to scheduled exports, using a dedicated account without write permission. Table and view details are confirmed per version.
- 2
Semantic layer
Maps estimates, jobs, operations, materials and work centers to fabrication vocabulary such as nest, remnant, bend, weld and outside process, including custom fields.
- 3
Model serving
An open-weight model served with Ollama or vLLM on a workstation-class GPU or small server on the shop network.
- 4
Retrieval and agents
Structured queries for numbers, with optional retrieval over job notes, quality records and attached customer specifications. Agents draft vendor follow-ups and estimate comparisons for approval.
- 5
Governance and audit
Per-user logging of questions, generated queries and answers, with customer or job exclusions where required.
Integration notes for your ERP team
- Use a dedicated read-only SQL login for the E2 database; avoid sharing the application's own credentials.
- Identify custom fields and material codes early, since fabricators often encode thickness, grade and finish in item numbers or user-defined fields.
- Nesting software such as SigmaNEST or Lantek keeps its own yield data; joining it to E2 is possible through exports and is an optional second phase.
- Indexed documents such as drawings and certs should come from approved folders only and respect the user's role.
- Use a replica or off-hours export if the E2 server is also running file services or other heavy workloads.
- No automatic write-back: vendor emails, price review lists and estimate comparisons are drafts that a named person approves.
Deployment options
On-premises in the shop
Fabricators with a local E2 server and customers who prohibit cloud AI on their drawings.
Model and connector run on the same network with no outbound path for ERP data.
Private cloud tenant
Shops with E2 hosted in an environment they control by contract.
A dedicated tenant next to the database with restricted egress and no shared AI services.
Hybrid with replica
Multi-plant fabricators or shops that want analysis kept off the production server.
A replica or export feeds the AI layer so daily operations are not slowed and each plant's data can stay local.
Compliance and data control
How the architecture supports your obligations. Certification and accountability stay with your organisation; the design keeps the evidence straightforward.
Customer NDAs and proprietary drawings
A model on infrastructure the shop controls means drawings, part numbers and pricing are not sent to a third-party AI provider. The shop can demonstrate the network path to its customers.
AWS D1.1 and ISO 3834 weld quality records
Where the shop records welder qualifications, WPS references and inspection results, the assistant can retrieve them for a job on request. It reports what is recorded and does not certify compliance.
EN 1090 and ISO 9001 documentation
Answers cite source records, and suggested process changes go through the existing document control procedure rather than being applied automatically.
ITAR for controlled defense work
Controlled jobs can be excluded from retrieval, or the whole system kept inside an environment limited to authorized persons on shop-owned hardware.
How an engagement runs
Phase 1 . 1-2 weeks
Discovery
- -E2 version and custom field inventory
- -Ranked questions from estimating, operations and customer service
- -Data access and deployment recommendation
- -Exclusion list for sensitive customers
Phase 2 . 4-6 weeks
Pilot
- -Read-only connector to production or a replica
- -Yield comparison and similar-job lookup live for estimators
- -Accuracy validation against known jobs
- -Logging reviewed with management
Phase 3 . 3-4 weeks
Production
- -Department bottleneck view and outside finishing tracker rolled out
- -Role-based access
- -Backup plan for the AI server
- -Admin runbook
Phase 4 . Ongoing
Improve
- -Price-change exposure review after material swings
- -Nesting data join if wanted
- -Model updates
- -Quarterly review of usage and accuracy
Questions to ask any vendor, including us
A short list that separates real E2 Shop System AI work from a chatbot demo.
- Is the database account read-only, and which tables can it see?
- Where does the model run, and how can we verify no drawing or pricing data leaves the network?
- How are material codes and custom fields mapped so a question about 14 gauge stainless resolves correctly?
- Can we see the past estimates and jobs behind a yield or hours suggestion?
- Will queries affect shop performance during the day?
- Which questions would our nesting software or E2's own reports already answer better?
- What do we keep if we end the engagement?
- Can we start with read-only answers before any drafting agents?
Frequently asked questions
Can AI make metal fabrication quotes more accurate in E2?
It can show the evidence faster. The assistant compares assumed material yield and hours to actual results on past jobs and finds similar parts, so an estimator can adjust. It does not read drawings or price jobs on its own in this setup, and accuracy depends on how consistently jobs were closed out in E2.
Does it replace our nesting software?
No. Nesting remains in SigmaNEST, Lantek or whatever you use. The AI layer compares the yield assumed in the estimate to what E2 shows was consumed, and can optionally pull nesting exports in to explain differences.
Is our customer data sent to a public AI service?
No. An open-weight model runs on a server you own or a private tenant, and the connector has no route to public AI APIs. That position is what most customer NDAs require, and you can verify the network path.
Can it help with material price swings?
Yes, as a review tool. After a price change it lists open quotes and orders whose margin is most exposed based on the material cost recorded. Repricing decisions stay with your team, and it only knows the prices recorded in E2.
What about welding quality records?
If your shop records welder qualifications, WPS references and inspection results in E2 or attached files, the assistant can retrieve them for a given job. It reports what is recorded and does not certify compliance with AWS D1.1 or ISO 3834.
Will it write to E2?
Not by default. It is read-only. It drafts vendor follow-ups, estimate comparisons and price review lists for a named person to approve, and anything entered into E2 goes through your normal process.
How long to get started?
A pilot with yield comparison and similar-job lookup can typically be running in one to two months, depending on data access approvals and how clean the job close-out data is. A wider rollout follows once users trust the answers.
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.
Global Shop Solutions + private AIAI for Global Shop Solutions, Built for How Job Shops Actually Run
Add AI to Global Shop Solutions without sending your quoting, scheduling, and job cost data outside your shop. Private, on-prem AI for GSS job shops.
Kinetic + private AIAI for Epicor Kinetic, Beyond What Prism Covers
Add AI to Epicor Kinetic beyond Prism: private LLM over BAQs, BPM data, and REST v2, on-prem or private cloud, with honest guidance on when Prism already covers you.
Ask your ERP anythingNatural Language Query for ERP Data: Ask SAP, Infor, or Oracle a Question in Plain English
See how natural language query over SAP, Infor, Oracle, and NetSuite data works: grounded text-to-SQL, role-based permissions, and a visible audit trail.
RAG + SQL + permissionsA Private LLM Grounded on Your ERP Data
How a private LLM answers questions on your ERP data: RAG plus text-to-SQL, role-based permissions inherited from the ERP, and where each fits.
Agents + approval gatesAI Agents for ERP, Running On-Prem
A practical guide to on-prem AI agents for ERP: what they can safely automate, where human approval belongs, and how to design the guardrails.
Plan it with numbers
On-Prem AI ROI Calculator
Turn hours saved per employee into annual net benefit, payback months, and 3-year ROI for an on-prem AI investment.
Free ToolOpen-Weight Model Selector
A 10-question assessment that matches your hardware budget, workload complexity, and operational maturity to the right open-weight model size class.
Free ToolSelf-Hosted LLM Hardware Estimator
Estimate the VRAM footprint, GPU count, and hardware budget required to self-host an open-weight LLM with your concurrency and context needs.
GuideAI-Optimized Production Scheduling for Manufacturing
Optimize production scheduling with AI. Reduce changeover time 30%, improve on-time delivery 20%, and maximize throughput using constraint-based optimization algorithms.
GuideAI-Powered Supplier Risk Assessment Tool
Assess supplier risk with AI-powered scoring. Monitor financial health, delivery performance, quality metrics, and geopolitical risk from ERP and external data sources.
GuideAI Inventory Optimization Algorithms for ERP
Optimize inventory with AI algorithms integrated into your ERP. Reduce carrying costs 20-35% while improving fill rates using reinforcement learning and demand sensing.
Talk it through with an engineer who knows E2 Shop System
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.