# AI for JobBOSS2 quoting and scheduling > Source: https://netray.co/erp-ai/jobboss2-quoting-scheduling-ai > Publisher: Netray (netray.co), AI and integration engineering for ERP and manufacturing systems. > Section: AI for ERP ## Short answer AI on JobBOSS2 compares a new request with similar past jobs, using quoted versus actual hours, material, and outside services, and helps schedulers see why a job will miss its date. It reads JobBOSS2 data without writing to it, so the estimator and scheduler stay in control. It can be hosted privately for shops with ITAR or customer confidentiality obligations. ## Kind use-case ## Erp Family other ## Erp Products - JobBOSS2 - ECI JobBOSS ## Industries - Job shops - Precision machining - Metal fabrication - Contract manufacturing ## Region Global ## Persona Estimator and Shop Owner ## Eyebrow JobBOSS2 + job shop AI ## Intro A job shop lives or dies on two decisions: what to quote and how to schedule. Both depend on knowing what similar work really cost and how long it really took. In many JobBOSS2 shops, that knowledge sits in the system but is only reachable by someone who knows which report to run, or by the owner who remembers the last time a similar part was made. JobBOSS2 holds quotes, jobs, routings, material, time and cost actuals, and the schedule. When an estimator gets a request for a new bracket, the useful question is: what did we quote and what did we actually spend on the three most similar parts? When a scheduler sees a hot job, the useful question is: what does moving it do to everything else? An assistant on top of JobBOSS2 can answer those questions from your data, show the jobs it used, and let the estimator or scheduler decide. It is not a quoting engine that invents prices. It is a faster way to see your own history. This page explains how that works on JobBOSS2, where the built-in tools are already good, and how to keep customer drawings and pricing private. Many JobBOSS2 shops serve aerospace, defense, and medical customers, and that matters for where the model runs. ## Pain Points ### Quotes built from memory **Detail:** Estimators lean on gut feel for similar parts because finding the last comparable job in the system takes too long. Margins suffer when the gut is wrong. ### Quoted versus actual is not reviewed **Detail:** Actual hours and material for completed jobs rarely feed back into the next quote, so the same underestimate repeats. ### Schedule conflicts found late **Detail:** A new hot job lands and the scheduler discovers the impact only after dates have been promised. ### Outside services slip **Detail:** Heat treat, plating, and other outside operations add lead time that is easy to miss in a quote or a schedule. ### Owner as the only search engine **Detail:** The shop owner or senior estimator knows which customers and parts are profitable. That knowledge is hard to hand over. ## Use Cases ### Similar job lookup for a new quote **Description:** The estimator describes a part, material, quantity, and operations. The assistant finds comparable past jobs, shows quoted and actual hours and cost, and highlights the spread. **Erp Objects:** Quotes, quote lines, jobs, routing operations, actual time and material costs, customer and part records **Outcome:** Estimators start with evidence from their own history. The quoted price remains the estimator's decision. ### Underquoted job pattern finder **Description:** Across closed jobs, the assistant identifies operations, materials, or customers where actuals regularly exceed the quote, and ranks them by dollar impact. **Erp Objects:** Closed jobs, quoted versus actual cost by operation, material price changes, customer **Outcome:** A shortlist of estimating rules to correct. Results depend on how consistently time is recorded. ### Quote follow-up and win-loss summary **Description:** The assistant lists open quotes by age and value and summarizes win or loss history by customer, so the sales owner knows where to follow up. **Erp Objects:** Quotes, quote status, sales orders, customer history **Outcome:** Fewer forgotten quotes and a clearer view of where pricing is working. ### Schedule impact of a hot job **Description:** The planner asks what happens if a rush job is started tomorrow. The assistant lists jobs that would slip, by how long, and by customer, from current schedule data. **Erp Objects:** Scheduled operations, work center load, due dates, job priorities **Outcome:** Trade-offs are visible before dates are promised. The schedule is changed in JobBOSS2 by a person. ### Late job root cause **Description:** For a job running behind, the assistant identifies the blocking step: waiting on material, an outside service, a busy work center, or a missing drawing revision. **Erp Objects:** Job operations, purchase orders and receipts, outside service operations, work center queue **Outcome:** Quicker recovery decisions with fewer calls to the floor. ### Material and outside service lead time check **Description:** Before a quote is finalized, the assistant checks supplier history for the material and outside services and flags long or unreliable lead times. **Erp Objects:** Purchase orders, receipts, vendor records, outside service operations **Outcome:** Quotes carry realistic delivery dates instead of optimistic ones. ### Weekly shop owner brief **Description:** A short summary of jobs at risk, quotes pending, and profitable or unprofitable work completed that week, with the data behind each line. **Erp Objects:** Open jobs, scheduled operations, quotes, closed job margin **Outcome:** The owner gets a consistent picture without building reports, and can ask follow-up questions in plain language. ## Architecture **Overview:** A read-only assistant sits beside JobBOSS2. It uses curated queries and similarity search over job history, and shows the jobs it based each answer on. JobBOSS2 remains the system of record for quotes, jobs, and the schedule. **Layers:** #### Source access **Detail:** A read-only path to the JobBOSS2 database or the integration interface your version supports. The exact method is confirmed in discovery. #### Job history index **Detail:** Past jobs are described by part attributes, material, operations, quantity, and customer, so similarity search can find comparable work. Drawings are indexed only if you choose to. #### Quoting and schedule queries **Detail:** Tested queries for quoted versus actual, open quotes, load by work center, and late jobs, with parameters filled by the model. #### Model serving **Detail:** An open-weight model on a workstation-class or server GPU, or a private cloud tenant. Drawings and pricing stay inside your boundary. #### Controls and audit **Detail:** User roles, a log of questions, and clear separation between suggestions and entries made in JobBOSS2. ## Deployment Options ### On-prem or private server beside the JobBOSS2 database **Fit:** Shops that will not let job, customer, or drawing data leave the building, including ITAR and CUI work. **Detail:** An open-weight model (Qwen, Llama, Mistral, or gpt-oss class) is served with vLLM or Ollama on a single GPU server on the plant network. Retrieval and query tools run on the same box. Small shops can often start on a single GPU workstation placed in a locked room. ### Sovereign or private cloud tenant **Fit:** Multi-site manufacturers that want managed GPUs without sending data to a shared public AI API. **Detail:** The model and the vector index run in a private cloud account or a regional sovereign provider, reached from the plant over a private link. Data stays in a tenant you control and you choose the region. ### Read-only pilot against a replica **Fit:** Teams that want to prove value on real data before committing hardware or touching production. **Detail:** A nightly restore or replica of the JobBOSS2 data feeds a small model and a handful of approved questions. Nothing writes back. Most of the learning is about data quality and which questions people actually ask. ## Compliance ### Private hosting keeps drawings and job data under the customer's control. The customer remains responsible for its registration and handling procedures. **Framework:** ITAR and export-controlled drawings ### Read-only access protects controlled records. The assistant cites job and routing data, but quality records are edited by authorized staff only. **Framework:** AS9100 and ISO 9001 records ### Where jobs contain CUI, the AI stack is placed inside the same enclave and access controls as JobBOSS2, with logging to support the customer's assessment. **Framework:** CMMC and NIST SP 800-171 for CUI ### Role-based visibility keeps one customer's pricing and parts out of answers for users who should not see them. **Framework:** Customer NDAs ## Integration Notes - Confirm which JobBOSS2 data path is available in your deployment before design. Database access, reporting views, and ECI-supported integration options differ by version and hosting. - Similarity search works best when part descriptions, material codes, and operation names are consistent. Plan time to normalize them. - Actual time capture quality sets the ceiling on quote feedback. Check shop floor data collection coverage before promising results. - Customer-specific price lists and contract terms should be treated as restricted data and excluded unless the user has the right role. - Outside service operations often carry lead times in purchase orders, not in routings. Include purchase order history in the data map. - Keep suggestions and entries separate. Quotes are created and approved in JobBOSS2 by the estimator. ## Product Fit ### ERPray **Why:** Plain-language questions about quotes, jobs, and schedule over JobBOSS2 data, read-only with the query and source jobs shown. ### Custom build **Why:** Right for a bespoke similar-job quote assistant that also reads drawings and customer specifications from a private document store. ### DataRay **Why:** Useful when drawings, certs, and quote emails sit on file shares and need to be searched alongside JobBOSS2 data. ## Engagement ### Discovery and data map **Duration:** 1 to 2 weeks **Deliverables:** - Inventory of the JobBOSS2 tables, views, and reports behind quoting and scheduling - Ranked list of 15 to 25 real questions from the people who do the work - Decision on hosting, access method, and which roles may see which data - Written read-only boundary and approval rules for any later write-back ### Grounding and retrieval build **Duration:** 2 to 4 weeks **Deliverables:** - Read-only connection with a service account scoped to the approved objects - Curated query layer and document index for quoting and scheduling - Evaluation set of questions with checked answers from your own data - Visible query or source citation on every answer ### Pilot with a small group **Duration:** 3 to 4 weeks **Deliverables:** - Pilot with 5 to 15 estimators, planners, supervisors, or quality staff - Weekly review of wrong or unhelpful answers and fixes - Usage log showing which questions were asked and which saved time ### Harden and hand over **Duration:** 2 to 3 weeks **Deliverables:** - Role mapping, audit logging, and backup of the AI stack - Runbook for model updates and index refresh after JobBOSS2 upgrades - Go or no-go note on any gated write-back actions ## Buyer Questions - Which JobBOSS2 version and hosting model do we have, and what data access path is available? - How does the assistant define a similar job, and can estimators adjust the criteria? - Does it set prices or only show history? - How is customer pricing separated by role? - What if our time data is incomplete? - Can drawings be included, and where are they stored? - Who approves anything that would change JobBOSS2 records? - What hardware do we need and can it run in our building? ## Faq ### Will the AI set our quote prices? **Answer:** No. It shows comparable past jobs with quoted and actual cost, and points out where estimates have been low. The estimator decides the price. Keeping a person in that loop avoids hidden errors and keeps accountability clear. ### How does it find similar jobs? **Answer:** It uses part attributes, material, operations, quantity, and customer, plus text descriptions. Consistent naming matters, so part of the pilot is cleaning up descriptions and operation names. Results are shown with the source jobs so the estimator can judge relevance. ### Is our customer data safe? **Answer:** The model runs on your hardware or a private cloud tenant and reads through a read-only path. Role-based limits keep one customer's pricing out of other users' answers. Logging records what was asked. ### Can it help with scheduling too? **Answer:** Yes, in an explanatory way. It shows the impact of a hot job, finds the blocking step on a late job, and highlights material and outside service risks. The schedule is changed in JobBOSS2 by the scheduler. ### What size shop is this for? **Answer:** Typically shops with enough job history to be useful, perhaps a few thousand completed jobs, and several people who quote or schedule. Smaller shops can still benefit, though the similarity results are less rich. ### What does it cost to run? **Answer:** Costs are mainly the project work and a GPU server or workstation, plus small ongoing maintenance. We would size hardware to your user count and model choice, and we avoid per-question public API fees. ### Is the built-in JobBOSS2 reporting enough? **Answer:** For standard reports, often yes. An assistant adds value when people ask ad hoc questions across quotes, jobs, and purchasing that would otherwise require a custom report or a call to the person who knows the system best. ## Search terms JobBOSS2 quoting AI, JobBOSS2 scheduling AI, AI quoting for job shops, JobBOSS2 quote versus actual, ECI JobBOSS2 AI assistant, job shop estimating AI, JobBOSS2 similar job lookup, JobBOSS2 late job analysis, private AI for machine shops, JobBOSS2 natural language query, JobBOSS2 integration AI --- Talk to an engineer: https://netray.co/contact?intent=erp_ai | Machine catalog: https://netray.co/agent/catalog.json