Job shop ERP + AI
AI for ECI JobBOSS2 and E2 Shop System
Short answer
You add AI to JobBOSS2 or E2 Shop System by reading job cost history, routings, and the scheduling board straight out of the SQL Server database that already runs your shop, then grounding a private language model on that data so it can draft quotes, flag at-risk jobs, and answer floor questions. Nothing needs to move to a public cloud API, and the model runs on hardware you own or control. For a shop with a handful of estimators and a scheduling board that changes hourly, the value shows up first in quoting speed and second in fewer 'where is my job' interruptions.
- ERP
- ECI JobBOSS2, E2 Shop System
- Industries
- Machining, Metal Fabrication, Contract Manufacturing, Job Shops
- Written for
- Owner
Most JobBOSS2 and E2 shops run lean: an owner or general manager, one or two estimators, a scheduler who is often also the owner, and a shop floor that runs on tribal knowledge about fixtures, tooling, and which operators are fast on which machines. The ERP holds the job cost history and routings that could answer half the questions people ask each other every day, but nobody has time to write a report, let alone a chatbot, to surface it.
The recurring pain is quoting. A new RFQ comes in, the estimator hunts for a similar part quoted two years ago, eyeballs the routing, and guesses at setup time because the person who ran that job left. That process takes hours per quote and the accuracy depends entirely on one person's memory. Meanwhile the scheduling board in JobBOSS2 or E2 shows what should happen, not what is actually happening on the floor, so expedites and late outside-process steps get discovered the hard way.
AI does not fix a job shop's fundamentals, but it removes the busywork around them. A model grounded on your own job cost history, routings, and open job data can draft a first-pass quote from the three most similar prior jobs, watch the schedule for jobs that are behind pace, and answer a machinist's 'what revision, what fixture' question from the traveler and document control attachments instead of a walk to the office.
Because JobBOSS2 and E2 both run on Microsoft SQL Server, the technical path is straightforward: a read-only reporting login, a data model that understands jobs, operations, and work centers, and a private model that never sends a customer's part number or pricing to an external API. That last point matters more than it sounds for shops with aerospace or defense customers who ask about data handling in their supplier surveys.
What usually gets in the way
The problems we hear most from owner teams running ECI JobBOSS2.
Quoting eats a full day per RFQ
Estimators re-derive routing and setup time from memory or by paging through old jobs, so quote turnaround depends on who is in the building that week.
The scheduling board lags the floor
JobBOSS2 and E2 scheduling boards show the plan, not what actually happened on the last shift, so a late outside-process step is usually discovered at the ship date, not three days before it.
Tribal knowledge is a single point of failure
Two or three long-tenured people know which fixtures, offsets, and setups work for which parts, and none of it is written down anywhere the ERP can surface.
Data is split across the ERP, QuickBooks, and paper
Job cost lives in the ERP, cash lives in QuickBooks or a separate accounting package, and travelers are still paper on many floors, so nobody has one place to ask a question.
The owner is the de facto expediter
Chasing a vendor for a late casting or confirming a subcontract heat treat delivery date falls to whoever has five minutes, usually after hours, because there is no dedicated purchasing follow-up.
Where AI earns its place in ECI JobBOSS2
Each use case names the ERP objects it reads or writes, so your ERP team can judge the integration effort before anyone commits budget.
Quote drafting from job cost history
Given a new part print or RFQ description, the assistant pulls the closest prior jobs by material, size, and operation sequence and drafts a first-pass routing and price for the estimator to adjust rather than build from scratch.
Touches: JobBOSS2 Estimating module, Job Cost History, Standard Routing and Operation records
Outcome: cuts first-pass quote turnaround from most of a day to under an hour for parts similar to prior work
Schedule exception triage
A daily agent reviews the dispatch list and work center loading, flags jobs that are behind the pace needed to ship on time, and explains why in plain language instead of leaving it buried in a Gantt view.
Touches: Job Scheduling board, Work Center capacity records, Dispatch List
Outcome: surfaces at-risk jobs at the start of the shift instead of after a machine has already sat idle
Shop floor traveler and document Q&A
Operators ask which revision is current, which fixture and offsets apply, or where a work instruction attachment is, and get an answer grounded in the traveler and document control record instead of walking to the office.
Touches: Routing and Traveler records, Document Control attachments, Work Instructions
Outcome: reduces office interruptions for routine revision and fixture questions
PO and expedite follow-up drafting
The assistant drafts routine supplier check-in emails for open purchase orders approaching their due date, so follow-up happens the same day instead of whenever someone has a free evening.
Touches: Purchase Order records, Vendor records, Receiving transactions
Outcome: gets routine expedite emails out same-day instead of after the fact
Outside process and subcontract tracking
Because plating, heat treat, and other outside operations are the most common cause of a missed ship date, the assistant watches subcontract operation due dates against job need dates and flags gaps early.
Touches: Job Material records, Subcontract/Outside Operations, Vendor lead times
Outcome: gives earlier warning on the outside-process steps that most often blow a ship date
New estimator ramp-up assistant
A new hire can ask the assistant how a similar part was quoted and routed before, shortening the time it takes to become productive without leaning on the one or two senior estimators for every question.
Touches: historical Job records, Standard Routings, Estimating notes
Outcome: shortens the time for a new estimator to quote independently
Job cost variance explanation
At job close, the assistant compares estimated to actual labor and material and explains the variance in plain language (setup ran long, extra outside-process pass, material substitution) rather than a raw spreadsheet delta.
Touches: Job Cost records, Labor Tickets, Estimated vs Actual comparisons
Outcome: turns a stack of variance numbers into reasons an owner can act on for future quotes
Reference architecture
Most JobBOSS2 and E2 shops do not have an IT department, so the architecture favors small, self-contained hardware over a data center build-out. A single workstation or small server with a modern GPU is enough to serve an open-weight model for a shop this size, reading directly from the SQL Server database that JobBOSS2 or E2 already runs on.
- 1
ERP connector
A read-only SQL login against the JobBOSS2 or E2 SQL Server database (or the E2 API where available), scoped to job, routing, scheduling, and purchasing tables only.
- 2
Data and semantic layer
A view layer that maps raw job, operation, and work center tables into plain-English concepts (job, part, routing step, due date) so the model reasons about shop terms, not column names.
- 3
Model serving
An open-weight model (Llama or Qwen class) served with Ollama or vLLM on a single workstation-class GPU, sized for a handful of concurrent users, not enterprise scale.
- 4
Retrieval and agents
Retrieval over job history and document attachments for quoting and Q&A; a scheduled agent for daily exception triage that writes a summary, not a database change.
- 5
Governance and audit
Every answer is logged with the query it ran and the source records it used, and no agent has write access to the ERP without a human approving the change first.
Integration notes for your ERP team
- JobBOSS2 runs on Microsoft SQL Server with a documented schema; a read-only reporting login is the standard, low-risk way in, and ECI's own reporting tools use the same approach.
- E2 Shop System also runs on SQL Server; where E2's newer releases expose a REST API, that is preferred over direct table reads for anything beyond simple reporting.
- Scheduling data changes throughout the day, so the exception-triage agent runs on a schedule (for example, start of each shift) rather than trying to stream every board update.
- Document control attachments (drawings, work instructions) are often stored as file paths or in a separate document management tool; the connector needs read access to that file share as well as the database.
- QuickBooks or a separate accounting package usually holds cash and AR/AP; if quoting or job cost questions need that context, a second, narrower connector is added rather than merging the two databases.
- Because most shops do not have in-house IT, the initial setup favors a vendor-managed appliance or workstation image over a from-scratch install the shop has to maintain.
- JobBOSS2 and E2 are not on Netray's list of pre-built ERP-agnostic connectors today, so this is delivered as a scoped custom build using the same on-prem model-serving stack used for other ERPs.
Deployment options
Single-workstation on-prem
Shops with 10-75 employees and no dedicated IT staff
A GPU-equipped workstation or small tower server in the office closet, running the model and reading the JobBOSS2/E2 database over the local network; no cloud dependency and minimal ongoing admin.
Private hosted instance
Multi-location shops or those who prefer someone else managing the hardware
A single-tenant private cloud instance connected back to the shop's database over a site-to-site VPN, keeping data isolated from other customers while removing local hardware upkeep.
Hybrid with cloud fallback for non-sensitive tasks
Shops that want general drafting help (e.g. marketing copy) alongside sensitive job data work
Job cost, pricing, and customer part data always stay on the local model; only non-sensitive general tasks are allowed to use a cloud model, with the split enforced by policy, not by trusting the user to remember.
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 supplier data handling surveys
Because job data, pricing, and part geometry are grounded on-prem, a shop can answer an aerospace or defense customer's supplier questionnaire honestly: quote and part data never leaves the building or a cloud API log.
ITAR (for shops making defense articles)
Technical data tied to a print or job stays on infrastructure the shop controls; no foreign national access path exists because there is no third-party model provider in the loop.
CMMC / DFARS flow-down (for DoD supply chain shops)
The same on-prem posture that protects job data also keeps any CUI referenced in a job packet inside the shop's own network boundary rather than a SaaS vendor's.
Basic access control
The assistant's database login is read-only and scoped to specific tables; anyone using it inherits the same visibility they already have in JobBOSS2 or E2, nothing more.
How an engagement runs
Phase 1 . 1-2 weeks
Discovery
- -Review of the JobBOSS2/E2 database schema and any customizations
- -Interviews with estimators, scheduler, and shop floor leads on the top 3 daily pain points
- -Hardware sizing recommendation for a single-workstation or small-server deployment
Phase 2 . 4-6 weeks
Pilot
- -Read-only connector to job, routing, scheduling, and purchasing data
- -Quote-drafting assistant tested against a set of recent real RFQs
- -Daily schedule exception summary delivered to the scheduler
Phase 3 . 2-4 weeks
Production rollout
- -Shop-floor Q&A access for operators via a simple kiosk or tablet interface
- -PO/expedite follow-up drafting turned on for purchasing
- -Access controls matched to existing JobBOSS2/E2 user roles
Phase 4 . ongoing
Scale
- -Job cost variance explanation added at job close
- -New estimator onboarding assistant tuned on the shop's own historical jobs
- -Quarterly review of what the assistant gets wrong and retraining of the retrieval set
Questions to ask any vendor, including us
A short list that separates real ECI JobBOSS2 AI work from a chatbot demo.
- Does the vendor read our data with a scoped, read-only login, or do they need a full copy of our database?
- Where does the model actually run, and can we see it running on hardware we control before we commit?
- What happens to a quote or part number we type in: does it ever leave our network, even temporarily, in a log or cache?
- Can the assistant write anything back to JobBOSS2 or E2 without a person approving it first?
- How much ongoing IT effort does this require from a shop with no dedicated IT staff?
- What is the real cost, including hardware, not just a subscription number?
- Can we start with just quoting or just scheduling, rather than buying the whole thing at once?
- If we outgrow the single-workstation setup, what does moving to a bigger deployment look like?
Frequently asked questions
Can AI actually quote a job accurately in JobBOSS2 or E2?
It drafts a first-pass routing and price from the closest prior jobs, which is a starting point for the estimator, not a final number. Accuracy depends on how similar the new part is to prior work and how clean the historical job cost data is. It removes the 'start from a blank page' step; the estimator still reviews and adjusts before it goes out.
Do we need to move to the cloud version of JobBOSS2 to use AI?
No. Both JobBOSS2 and E2 run on a SQL Server database that a private, on-prem model can read directly, whether the shop is on a cloud-hosted or locally hosted instance of the ERP. The AI layer is separate from where JobBOSS2/E2 itself runs.
Is our job data safe if we let an AI assistant read it?
With an on-prem deployment, the model and the data stay on hardware the shop controls, and the database login is read-only and scoped to specific tables. Nothing is sent to a third-party API by default, which is the main safety difference versus using a public chatbot with copy-pasted job data.
How much does this cost for a shop our size?
For a single-workstation on-prem setup serving a handful of users, the main costs are the GPU-equipped hardware (a one-time cost) and the implementation work to build the connector and tune it on your data. There is no per-seat SaaS-style subscription required if you run the model yourself, though ongoing support is typically a smaller recurring fee.
Will this replace our estimator or scheduler?
No. It removes the repetitive lookup work, drafting, and status-chasing that eats their day, but the estimator still owns the final quote and the scheduler still owns the final call on sequencing. Small shops do not have spare headcount to remove; the goal is giving the people you have back their time.
Does it integrate with QuickBooks too?
Not by default. The initial build focuses on JobBOSS2 or E2 job, routing, and purchasing data. A QuickBooks or accounting-package connector can be added separately if quoting or reporting needs that financial context, but it is scoped as its own piece of work.
What if our shop has heavy customizations to JobBOSS2 or E2?
Discovery includes a review of the actual schema in use, including custom fields and tables, because many shops have added their own tracking over the years. The connector and semantic layer are built against what your database actually looks like, not a generic template.
Related guides
AI 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.
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.
SYSPRO + private AIAI for SYSPRO, Grounded in Your Own Manufacturing Data
Add AI to SYSPRO without moving your manufacturing data to a public model. On-premise and private-cloud LLMs for SYSPRO in Australia, NA, and beyond.
CIO playbook: strategy, budget, orgA CIO's Guide to Sequencing AI on Your Manufacturing ERP
A practical CIO guide to sequencing AI on top of your manufacturing ERP: where to start, how to budget, who should own it, and how to avoid a pile of stalled pilots.
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 ToolAI Project Cost Estimator
Turn project scope, integration count, data readiness, and team weeks into a defensible AI project budget with contingency built in.
Free ToolAI Use Case Value vs Effort Calculator
Turn each AI idea into annual net value, build cost, payback period, and three-year ROI so your backlog is prioritized on economics instead of enthusiasm.
GuideAI-Powered Quoting for Aerospace & Defense Manufacturers
AI-powered quoting for aerospace and defense manufacturers: cut quote turnaround from weeks to days while handling ITAR data, DFARS clauses, and cost buildup.
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 Agent Architecture for Enterprise ERP Systems
Design AI agent architectures for ERP systems with multi-agent orchestration, tool-use patterns, memory management, and enterprise integration strategies.
Talk it through with an engineer who knows ECI JobBOSS2
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.