Specialist ERPsIndustry

JobBOSS2 + precision machining

AI for JobBOSS2 in Precision Machining: Quotes, Routings and Job Cost Answers from Your Own Data

Short answer

AI for a JobBOSS2 precision machine shop connects a private model to the shop's quote, job, routing and labor history so estimators, schedulers and owners can ask plain-English questions such as which part families run over estimate and why. It runs read-only on your network, shows the query behind each answer, and keeps customer drawings and pricing out of public AI services.

ERP: ECI JobBOSS2, JobBOSS
Industries: Precision Machining, Contract Manufacturing
Written for: Shop Owner / General Manager

Discuss your ECI JobBOSS2 environment See use cases

A precision machining shop that runs JobBOSS2 usually has ten or more years of quotes, jobs, routings and labor tickets sitting in one database, and the owner's real competitive edge is buried in it: which part families actually make money, which customers' tolerances eat setup time, which machines run a given material faster than the routing says. Most of that knowledge lives in the head of one estimator and one scheduler.

The problem is not a lack of data. JobBOSS2 captures planned versus actual hours by operation, material and outside service costs by job, and delivery performance by customer. The problem is the effort of pulling it together. Standard reports answer the questions someone anticipated, and the questions that move margin, such as why this family of 17-4 stainless housings keeps costing 15 to 25 percent more than quoted, need a person with SQL skills and an afternoon.

Precision shops are also cautious about where their data goes. Customer prints, tolerances and part numbers are often covered by NDAs, and some work carries export-control flow-downs. Pasting a job history into a public chatbot is exactly what most quality managers and owners have told staff not to do, so an AI layer has to run on infrastructure the shop controls.

This page describes what private AI on top of JobBOSS2 looks like for a machining shop: which records it reads, the use cases that pay back first, where JobBOSS2's own reporting is enough, and the questions to ask any vendor before connecting it to a production database.

What usually gets in the way

The problems we hear most from shop owner / general manager teams running ECI JobBOSS2.

Quotes are priced from memory, not from history

JobBOSS2 holds past quotes and actual job costs, but comparing a new RFQ to the five most similar past jobs means digging through screens. Estimators default to gut feel and the last number they remember, so under-quoting on repeat work goes unnoticed for months.

Routing standards drift from what the floor really does

Planned setup and run times on the routing are set once and rarely revisited. Actual labor tickets show where the routing is wrong, but nobody has time to analyze them operation by operation across hundreds of part numbers.

Scheduling depends on one person

Which job to run next on which machine, given material arrival, outside processing and a customer promise date, is usually decided by a scheduler who has the whole shop in their head. When that person is out, on-time delivery slips.

Job cost variance is found after the job ships

Variance reports tell you a job lost money once it is closed. The causes, such as a scrapped first lot, an extra setup, or a tool failure, sit in notes and labor tickets that are never read together.

Customer-facing questions interrupt production

Customer service and sales ask the shop floor where a job stands, whether a material cert has arrived and what the real ship date is. Each answer pulls a supervisor off a machine.

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.

Similar-job quote assistant

An estimator pastes an RFQ's material, size, quantity and operations and the assistant returns the closest past quotes and completed jobs with their quoted versus actual hours and costs, flagging where history says the estimate is likely light.

Touches: JobBOSS2 quotes, quote line items, completed jobs, job materials, operations, labor tickets

Outcome: Gives estimators evidence to adjust a quote before it is sent, which tends to reduce repeat under-quoting on recurring part families.

Routing actual-versus-standard review

The assistant ranks operations by the gap between planned and actual setup and run time across completed jobs, by work center and material, and proposes routing standard updates for a human to approve.

Touches: Routings, job operations, work centers, labor and machine time records

Outcome: Replaces a periodic spreadsheet exercise with a standing list of the routing standards most worth correcting.

Job variance explainer

For a closed or in-process job over estimate, the assistant summarizes where hours and cost went, by operation, material, scrap and outside service, and quotes the notes that explain it.

Touches: Job cost summary, material issues, labor tickets, purchase orders for outside services, job notes

Outcome: Shortens the post-job review from a manual hunt to a few minutes and makes lessons easier to feed back into quoting.

Promise-date risk list

Each morning the assistant lists open jobs likely to miss their due date, with the blocking reason: material not received, outside processing late, or an upstream operation behind.

Touches: Open jobs, operations, scheduled dates, purchase order lines, material receipts

Outcome: Moves the scheduler from scanning screens to working an exception list, so customer calls are made before the date is missed rather than after.

Customer status answers for the office

Customer service asks in plain English where an order is, whether certs are in, and when it ships, and gets an answer built from live job and shipping data without calling the floor.

Touches: Sales orders, jobs, operations completed, receiving records, packing and shipping records

Outcome: Removes a recurring source of supervisor interruptions during the shift.

Outside processing follow-up drafts

The assistant finds outside service purchase orders (heat treat, anodize, plating, grinding) past their promised return date and drafts a follow-up to the vendor for the buyer to review and send.

Touches: Purchase orders, outside service operations, vendors, job due dates

Outcome: Turns a manual daily check into a short queue of reviewed follow-ups.

Profitability by customer and part family

Owners ask which customers, materials or part families earn the best margin after setup, scrap and rework, and see the supporting jobs rather than a single opaque score.

Touches: Invoices, job costs, quotes, customers, parts, scrap and rework records

Outcome: Gives pricing and capacity decisions a factual basis, such as which small-lot customers are not covering their setup time.

Reference architecture

The AI layer reads JobBOSS2's data through a read-only database account or supported export path on your network, maps it to machining vocabulary, and answers with the generating query visible. Any proposed change, such as a routing update or a vendor email, is a draft for a person to approve.

1
JobBOSS2 data access
Read-only access to the JobBOSS2 database, which sits on Microsoft SQL Server, or to scheduled exports, using a dedicated account with no write permission. The exact tables and views are confirmed per version during discovery.
2
Semantic layer
Maps jobs, quotes, routings, work centers and materials to shop terms such as repeat part, first article, outside process and setup, including any custom fields or user-defined codes the shop added.
3
Model serving
An open-weight model such as Llama, Qwen or Mistral class served with Ollama or vLLM on a single workstation-class GPU or a small server, which is enough for the question volume of a shop with tens of users.
4
Retrieval and agents
Structured queries for numbers, plus retrieval over job notes, NCR text and attached specifications where the shop wants them included. Agents draft; people approve.
5
Governance and audit
Every question, query and answer is logged by user, and sensitive customer or part groups can be excluded from retrieval.

Integration notes for your ERP team

Use a dedicated read-only SQL Server login scoped to the JobBOSS2 database; never reuse the application's service account.
Confirm the JobBOSS2 version and any custom fields up front, because the semantic layer must reflect what the shop really records, not a generic schema.
Run heavy analytical queries against a replica or off-hours export when the shop's server is also running the application and file shares.
Index attached documents such as material certs and customer specs only from approved folders, with access mapped to the user's role.
Machine monitoring data (cycle times from a CNC monitoring system) can be joined to job operations where the shop has it, but is an optional second phase.
Write-back is limited to drafts: proposed routing changes, vendor emails and NCR text that a named person approves before anything reaches JobBOSS2.

Deployment options

On-premises server in the shop

Shops with a local JobBOSS2 server and customer NDAs that prohibit cloud AI.

The model and connector run on a small server on the same network, with no outbound path for ERP data.

Private cloud tenant

Shops whose JobBOSS2 database already runs in a hosted environment under the owner's own agreement.

A dedicated tenant next to the database, isolated from shared AI services and with outbound traffic restricted.

Hybrid with a read replica

Shops that do not want analytical queries hitting the production database during the day.

A nightly or intraday read replica or export feeds the AI layer, so production performance is unaffected and answers are no more than a few hours old.

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 drawing controls

Because the model runs on infrastructure the shop controls, customer prints, tolerances and pricing are not sent to a third-party AI provider, which is the position most NDAs require. Netray does not claim the shop's compliance for it; the architecture lets the shop demonstrate it.

ITAR and EAR flow-downs

Shops handling controlled technical data can keep the model, retrieval index and logs inside a U.S.-person-only environment on their own hardware, and exclude controlled jobs from retrieval entirely if required.

ISO 9001 and AS9100 record control

Answers are traceable to source records, and any suggested change to routings or standards goes through the shop's existing document and change control rather than being applied automatically.

CMMC for defense supply chains

Where a shop handles CUI, the AI layer is kept inside the same enclave boundary as the ERP, and its access control and logging are documented so they can be included in the shop's own system security plan.

Where Netray fits

ERPray

Natural-language questions and dashboards over JobBOSS2 job, quote and routing data, read-only, showing the query for each answer.

Custom build

Shops with a bespoke quoting method or a monitoring system to join in often need a custom connector and a small agent for quote assistance.

How an engagement runs

Phase 1 . 1-2 weeks

Discovery

-JobBOSS2 version and custom field inventory
-Ranked list of five to ten questions the owner and estimator want answered
-Data access and deployment recommendation
-Exclusion list for sensitive customers or jobs

Phase 2 . 4-6 weeks

Pilot

-Read-only connector against production or a replica
-Similar-job quote assistant and variance explainer live for a small group
-Accuracy check of answers against known jobs
-Query logging reviewed with the shop

Phase 3 . 3-4 weeks

Production

-Promise-date risk list and customer status answers rolled out
-Role-based access configured
-Backup and restore plan for the AI server
-Admin runbook

Phase 4 . Ongoing

Improve

-Routing standard review cycle
-Outside processing follow-up drafts
-Model updates as better open-weight options appear
-Quarterly review of questions asked and answer quality

Questions to ask any vendor, including us

A short list that separates real ECI JobBOSS2 AI work from a chatbot demo.

Does the AI use a read-only account, and can we see exactly which tables or views it can read?
Where does the model run, and can we verify that no job, quote or customer data leaves our network?
How are our custom fields and user-defined codes handled when answers are generated?
Can an estimator see the past jobs behind a suggested hours figure, not just a number?
Will queries slow the production database during the day, and is a replica an option?
What happens to the system and its logs if we stop working with you?
Is JobBOSS2's own reporting enough for some of these questions, and where would you say so honestly?
What hardware is required, and can we start with question answering before agents that draft documents?

Frequently asked questions

Can AI improve quoting accuracy in a JobBOSS2 machine shop?

It can make the evidence easier to use. The assistant finds the closest past quotes and completed jobs and shows quoted versus actual hours and cost, so an estimator can adjust before sending. It does not replace estimating judgment, and accuracy depends on how consistently past jobs were costed and closed in JobBOSS2.

Is JobBOSS2's built-in reporting not enough?

For standard reports it often is, and we would say so. AI adds value on ad hoc questions that need joining quotes, routings, labor tickets and notes, such as why a part family keeps running over estimate. If your questions are all covered by existing reports, you may not need it.

Does our data go to a public AI service?

No. The model is an open-weight model served on your own server or private tenant, and the connector has no route to public AI APIs. Customer drawings, tolerances and pricing remain on your network, which is what most NDAs and export-control flow-downs require.

How big a server do we need?

For a shop with tens of users asking occasional questions, a single workstation-class or entry server GPU is typically enough for a mid-size open-weight model. Sizing depends on the model chosen, document volume and concurrency, and is confirmed in discovery rather than assumed.

Can it change our routings or jobs automatically?

No. By default it is read-only. It can draft a proposed routing standard change or a vendor follow-up email, but a named person approves it and enters it through your normal JobBOSS2 process. Nothing is written to the database by the AI layer.

What if we customize JobBOSS2 with user-defined fields?

That is common and is handled in the semantic layer. During discovery we inventory the custom fields and codes your shop uses and map them to plain terms, so a question about a custom material class or customer code resolves correctly.

How long until it is useful?

A read-only pilot with a few use cases, typically similar-job lookup and a variance explainer, can be live in roughly one to two months depending on data quality and access approvals. Wider rollout follows once the shop trusts the answers.

Related guides

Job shop ERP + AI

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 AI

AI 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.

Global Shop Solutions + private AI

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.

Ask your ERP anything

Natural 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 + permissions

A 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.

ERP AI readiness

Is Your ERP Ready for AI? A Readiness Assessment Checklist

Is your ERP actually ready for AI? A practical checklist covering master data quality, access and permissions, GPU sizing, and governance before you fund a pilot.

Plan it with numbers

Free Tool

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.

Start the conversation See ERPray

All AI for ERP guides