Epicor Kinetic quoting AI
AI for Epicor Kinetic Estimating and Quoting: Faster Quotes Grounded in Your Own Job History
Short answer
AI for Epicor Kinetic estimating reads an RFQ, finds similar past parts and jobs, and prepares a draft quote with materials, operations and quantity breaks for an estimator to check. It compares planned cost with job actuals so estimates improve over time, uses the Kinetic REST API and BAQs, and can run on-prem so customer drawings and pricing stay private.
- ERP
- Epicor Kinetic
- Industries
- Job Shop, Discrete Manufacturing, Metal Fabrication
- Written for
- Estimating Manager
In a job shop, quoting is where margin is won or lost, and it is usually the most manual process in the building. An RFQ arrives as a PDF drawing and an email. An estimator reads it, thinks of a similar part, opens Quote Entry, builds a quote assembly with materials and operations, picks quantity breaks, and applies burden and markup. On a good day this takes an hour. With twenty RFQs in the queue, the quotes that go out late are the ones that lose.
Epicor Kinetic has the structure for good estimating. QuoteHed, QuoteDtl, QuoteQty, QuoteAsm, QuoteMtl and QuoteOpr hold a full cost build-up, resource groups carry labor and burden rates, and actuals from jobs sit in JobHead, JobOper and LaborDtl. The difficulty is that the knowledge connecting an RFQ to the right past job is in the estimator's head, and the comparison of quoted to actual cost happens rarely if at all.
AI can help at three points: reading the RFQ and its drawing into structured requirements, finding the most similar parts and jobs already in the system, and checking a draft against what similar jobs actually cost. It is not a substitute for estimator judgment, particularly on new processes, tolerances and risky materials. The aim is to remove retyping and searching, so the estimator spends time on the decisions.
This page covers how that works with Kinetic's APIs, what to do about drawings and controlled data, and where Kinetic's product configurator or a simple quote template is the better tool.
What usually gets in the way
The problems we hear most from estimating manager teams running Epicor Kinetic.
Quoting is slow and person-dependent
Quote Entry is flexible, so each estimator builds quotes differently. The best estimators are bottlenecks, and turnaround time depends on who is in the office.
Similar jobs are hard to find
Searching Part, JobHead or QuoteHed by description misses jobs with different part numbers but the same process. Estimators reuse what they remember rather than the closest match.
Estimates are never compared to actuals
Planned hours and material on JobOper and JobMtl are rarely compared with LaborDtl and PartTran actuals. Systematic underestimates on a process persist for years.
RFQ data entry is repetitive
Part number, revision, quantities, material, finish and due dates are retyped from PDFs and emails. Mistakes at this step flow into every later document.
Rates and burden drift
Resource group rates, burden and markup rules are updated unevenly. Quotes built months apart use different assumptions for the same operation.
Where AI earns its place in Epicor Kinetic
Each use case names the ERP objects it reads or writes, so your ERP team can judge the integration effort before anyone commits budget.
RFQ intake to draft quote header
Extracts customer, part number, revision, quantities, material and delivery from an RFQ email and attachments, matches the customer and part, and prepares a quote header for review.
Touches: QuoteHed, QuoteDtl, QuoteQty, Customer, Part, PartRev
Outcome: Removes most retyping at intake. The estimator verifies extracted values before saving.
Similar part and job search
Given a drawing description or an existing part, finds the closest past parts, quotes and jobs by material, size, features and operations, and shows what they cost.
Touches: Part, PartOpr, PartMtl, JobHead, JobAsmbl, JobOper, QuoteAsm
Outcome: Estimators start from a relevant precedent instead of memory. Quality depends on how well past records are described.
Draft method of manufacture
Proposes a bill of materials and routing for a new part by adapting the nearest match, with operations, resource groups and setup and run times flagged for estimator confirmation.
Touches: QuoteAsm, QuoteMtl, QuoteOpr, QuoteOpDtl, ResourceGroup, OpMaster
Outcome: A starting method in minutes. Estimators adjust rather than build from blank.
Estimate versus actual feedback
Compares quoted and job-costed hours and material by operation and part family, and recommends adjustments to standards where the gap is consistent.
Touches: JobOper, JobMtl, LaborDtl, PartTran, QuoteOpr, JobHead
Outcome: Surfaces systematic under- or over-estimates, which improves margin accuracy over successive quotes.
Quantity break and price sanity check
Reviews a draft quote for quantity breaks that do not make sense, margins below a threshold, or prices out of line with similar recent wins and losses.
Touches: QuoteQty, QuoteDtl, OrderDtl, CustCnt, quote win-loss status
Outcome: Fewer margin mistakes escape. Pricing policy and the final price remain the estimator's and manager's decision.
Material and subcontract price lookup
Pulls recent purchase prices for materials and outside processes from receipts and purchase orders, and flags stale cost data in the quote.
Touches: PODetail, RcvDtl, PartCost, SupplierPart, QuoteMtl
Outcome: Material cost in quotes tracks recent buys more closely, which matters when commodity prices move.
Quote follow-up and win-loss analysis
Lists open quotes by age and value, drafts follow-up emails, and summarizes why quotes were lost by customer, part family and price gap.
Touches: QuoteHed (Quote Status), QuoteDtl, Customer, Sales order history
Outcome: More consistent follow-up and a clearer view of where price, lead time or capability loses work.
Reference architecture
The assistant sits beside Kinetic. It uses the Kinetic REST API and BAQs to read parts, quotes and jobs, processes RFQ documents in a private pipeline, and writes only draft quotes through the quote business object after estimator approval. Drawings stay in the customer's environment.
- 1
Document intake
Email and file-share watchers feed RFQ emails, PDFs and drawings through text extraction and, where needed, OCR, hosted on-prem for controlled data.
- 2
ERP connector
Kinetic REST v2 (OData) for business objects such as Erp.BO.QuoteSvc, Erp.BO.PartSvc and Erp.BO.JobEntrySvc, plus BAQs for aggregated cost and history queries, authenticated with an API key and a service user.
- 3
Similarity index
A vector and attribute index over parts, routings and past quotes, built from descriptions, material, operations and dimensions, refreshed on a schedule.
- 4
Model and reasoning
A self-hosted open-weight model with a vision-capable option for drawing title blocks and notes, calling tools for lookups and producing structured draft quotes.
- 5
Review and write-back
A review screen shows extracted data, matched precedents and the cost build-up. Only on estimator approval is the draft written through the quote business object, with a log of what the model suggested.
Integration notes for your ERP team
- Kinetic REST v2 exposes business objects at /api/v2/odata/{company}/Erp.BO.{Name}Svc; authenticate with an API key plus basic or token credentials, and use a dedicated service user.
- BAQs (Business Activity Queries) are the preferred way to expose cost and history aggregates, and can be called via the BAQ service with parameters, which avoids pulling raw job detail.
- Quote creation involves QuoteHed, QuoteDtl, QuoteQty, QuoteAsm, QuoteMtl and QuoteOpr; use the business object methods so Kinetic business logic and BPMs run, rather than writing tables.
- Custom UD fields on parts and quotes can carry fields such as material spec and finish; include them in BAQs so the similarity index can use them.
- Epicor Functions and BPMs can validate drafts at save, for example enforcing a minimum margin, so the assistant cannot bypass company rules.
- Drawings often live in Epicor attachments or a file share; plan the document path and permissions early, since this is a common delay.
Deployment options
On-premises server
Shops handling ITAR, CUI or customer-confidential drawings
GPU server in the customer's facility alongside Kinetic. RFQs, drawings and pricing never leave the site.
Private cloud
Kinetic Cloud or private-cloud customers
Model and index run in a dedicated tenant in the customer's chosen region, connecting to Kinetic over its API with IP allow-listing.
Estimator pilot
Shops testing with one product family
A read-only search and draft tool for one team, using a sample of past quotes. Writes begin after the accuracy review.
Compliance and data control
How the architecture supports your obligations. Certification and accountability stay with your organisation; the design keeps the evidence straightforward.
ITAR and EAR
Controlled drawings are processed only on infrastructure and by personnel the customer designates. The architecture permits an air-gapped model; the customer remains responsible for authorization.
NDA and customer confidentiality
Quote data, drawings and pricing stay in the customer environment. No training on customer data by third parties, and retention is configurable.
CMMC and NIST SP 800-171
For shops handling CUI, the system is deployed inside the customer's assessed enclave, with access control, logging and encryption configured to the customer's boundary.
Quality system records (AS9100, ISO 9001)
Contract review is a quality requirement. The log of RFQ review, estimator approval and changes supports the record that review took place.
How an engagement runs
Phase 1 . 1-2 weeks
Quote process review
- -Sample of 100 to 200 past quotes and jobs
- -Estimator interviews
- -Data quality assessment
- -Success measures
Phase 2 . 3-4 weeks
Search and cost feedback
- -Similarity index over parts and quotes
- -Estimate versus actual reports
- -Read-only assistant for estimators
- -Accuracy review
Phase 3 . 4-6 weeks
RFQ intake and drafts
- -RFQ extraction pipeline
- -Draft quote creation through business objects
- -Review screen
- -Approval and margin rules
Phase 4 . 2-3 weeks
Production and tuning
- -Production deployment
- -Monitoring of acceptance rate
- -Estimator training
- -Tuning plan
Questions to ask any vendor, including us
A short list that separates real Epicor Kinetic AI work from a chatbot demo.
- How does the assistant find similar jobs, and can it explain why it considers them similar?
- Where are drawings processed, and can this run fully on-prem?
- Does it write through Kinetic business objects so BPMs and validations still run?
- How do estimators correct a wrong suggestion, and does the system learn from corrections?
- What data quality do we need in past quotes and jobs for this to work?
- How do you handle UD fields and our custom quoting process?
- What happens when Kinetic is upgraded?
- Would Kinetic's configurator or quote templates solve this more cheaply?
Frequently asked questions
Can AI generate a complete quote in Epicor Kinetic?
It can prepare a draft with parts, materials, operations and quantity breaks based on similar past work, but an estimator should review before it is sent. New processes, tight tolerances and unusual materials need human judgment. The most reliable gain is in repeat or near-repeat work, where the draft is close and the estimator corrects details.
Does it need clean data?
It needs reasonably consistent data, not perfect data. Parts with descriptive names, routings that reflect real operations and jobs closed with actual hours make the search and comparisons better. A data review in the first phase shows where weak records limit results and whether some cleanup is worth doing first.
Is Kinetic's own product configurator enough?
For products built from defined options, a configurator is usually the better, more deterministic tool. Job shops with one-off parts do not have fixed options, so similarity search and drafting help more. Some companies use both, with the configurator for catalog products and AI assistance for custom work.
How are drawings handled?
Drawings are processed in the same environment as the rest of the system. For controlled work, that means an on-premises or air-gapped deployment. The model reads title block data, notes and visible features, but it does not replace an estimator's reading of tolerances, so critical dimensions are flagged for human check.
Will it create quotes without approval?
Not by default. The assistant writes drafts only after an estimator accepts them in the review screen, and Kinetic business logic and BPMs still run on save. Auto-quoting for narrow, repeat items is possible, with price limits and exception rules, but that is a later step once the accuracy record exists.
How does it improve estimates over time?
By comparing quoted hours and material with actuals from job costing, it highlights operations or part families where estimates are consistently off. People then update standards or resource group rates. The model is not retrained on every job; the improvement comes from better inputs and clearer feedback.
What does Kinetic upgrade cycle mean for this?
Kinetic REST business objects are generally stable across releases, but custom BAQs, UD fields and BPMs need a regression check after each upgrade. We recommend testing in a pilot environment on the new version before production. Keeping the integration on supported APIs, not direct SQL, keeps this manageable.
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Talk it through with an engineer who knows Epicor Kinetic
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.