SyteLine quoting + private AI
AI for SyteLine Sales Quoting and Estimating That Learns From Your Own Won and Lost Jobs
Short answer
AI for SyteLine quoting reads estimate, customer order and job history through IDOs or read-only SQL, finds comparable past work, and drafts a cost build-up with margin and lead-time checks for an estimator to approve. Nothing is written to the quote or customer order without a human clicking approve, and the model can run on the customer's own GPUs.
- ERP
- Infor SyteLine, Infor CloudSuite Industrial
- Industries
- Industrial Machinery, Fabrication, Engineer-to-Order, Aerospace
- Written for
- Sales Operations Manager
A quote in SyteLine is rarely just a price lookup. For a made-to-order or engineered product, an estimator assembles material, routing, outside processing, tooling and markup, often by copying an old estimate and editing it. The knowledge about what a similar job really cost lives in closed jobs, in variance reports nobody opens, and in the heads of two or three senior estimators. When one of them leaves, quote accuracy quietly drifts.
SyteLine already gives you the pieces: the Estimates form with estimate lines and templates, Customer Orders with order lines and pricing, price codes and customer-specific price matrices, item and routing masters, and job costing once an order ships. What it does not give you is a fast way to ask, in plain language, what the last five similar jobs actually cost, how long they actually took on the shop floor, and what margin they landed at after scrap and rework.
Sales operations teams also carry a second burden: quote follow-up and pipeline hygiene. Open quotes age, customers ask for revisions, and there is no clean view of win rate by customer, product family or estimator. Reports exist, but building a new one means a ticket to IT or a SQL-capable consultant.
This page explains how AI sits on top of SyteLine quoting and estimating, which objects it reads, what it can safely draft, and where a plain SyteLine report or the built-in pricing rules are still the better tool. It is written for sales operations and estimating leads, not for a data-science audience.
What usually gets in the way
The problems we hear most from sales operations manager teams running Infor SyteLine.
Estimates copied from old jobs without checking actual cost
Estimators clone a prior estimate and adjust quantities. The estimate carried the planned cost, not the actual cost after scrap, rework and overtime, so the same under-quote repeats job after job.
Tribal knowledge about what a job really takes
Two senior estimators know that a certain alloy or tolerance always runs long on a particular work center. That knowledge is not in the routing, so a newer estimator quotes the standard hours and loses margin.
Slow turnaround on complex quotes
Pulling material prices, vendor lead times, outside processing quotes and routing hours together can take days. Customers who send the same RFQ to several suppliers often award to whoever answers first.
No feedback loop from won and lost quotes
Win and loss reasons are rarely captured as structured data, so pricing decisions are made without evidence of where the company is consistently too high or too low.
Margin leakage from stale prices
Material cost and price matrices go out of date between quote and order. Quotes validated months ago convert to orders at today's costs and the margin gap appears only at job close.
Where AI earns its place in Infor SyteLine
Each use case names the ERP objects it reads or writes, so your ERP team can judge the integration effort before anyone commits budget.
Comparable-job search for estimators
Describe the part in plain language or paste the RFQ line and get the closest past estimates and jobs by item family, material, size and routing, with quoted versus actual cost side by side.
Touches: Estimate headers and lines, Customer Orders (co, coitem), Jobs, job material and job routing transactions, Item master
Outcome: Estimators start from evidence instead of a blank form. Time to a first draft typically drops from hours to minutes on repeat-family work.
Draft cost build-up for approval
The agent proposes material, labor, outside processing and overhead lines from the comparables, flagging every assumption. The estimator edits and approves; nothing posts without that click.
Touches: Estimate lines, Item and BOM masters, Routing and Work Center rates, Outside processing items
Outcome: Fewer missed cost elements such as outside processing or tooling, and a consistent structure across estimators.
Margin and price sanity check
Compares the draft price with customer price codes, price matrix entries and historical margin on similar work, and warns when the quote sits well outside the usual band.
Touches: Price codes, customer price matrices, Customer master, Customer order line pricing, Job cost summary
Outcome: Catches obvious under-quotes before they leave the building. It does not set strategy, it flags outliers.
Lead-time realism check
Reads current work center load, open job backlog and vendor lead times to say whether the promised date is credible, and shows which operation is the constraint.
Touches: Work center load, open jobs and operations, Purchase order lines and promised dates, Item lead-time fields
Outcome: Fewer promised dates missed on quotes that convert. Quality depends on how well routing and lead-time fields are maintained.
Quote follow-up and revision drafting
Surfaces open quotes past a set age, drafts a follow-up note in the salesperson's voice, and summarises what changed between quote revisions.
Touches: Estimates and quote status, Customer Orders, Customer contacts, Order change history
Outcome: More consistent follow-up and fewer quotes that simply expire. The salesperson still sends the message.
Win/loss analysis from structured history
Answers questions such as win rate by product family, average discount at conversion, or which customers repeatedly ask for requotes, using the data already in the estimate and order tables.
Touches: Estimates converted to orders, Customer Orders, Customer master, Reason codes where captured
Outcome: Replaces ad hoc spreadsheet exports with questions answered in minutes, with the query shown so the analyst can verify it.
RFQ intake to estimate shell
Reads an RFQ PDF or email, extracts part numbers, quantities and required dates, and creates a pre-filled estimate shell for the estimator to review, never submitting it automatically.
Touches: Estimates, Item master, Customer master, Document attachments
Outcome: Removes re-keying from the first step of quoting. Extraction accuracy varies with document quality, so review is mandatory.
Reference architecture
The AI layer reads SyteLine through IDOs, the REST layer exposed by Mongoose, or read-only SQL views on a replica. A retrieval index of closed jobs and estimates supports the comparable-job search. Any write is a proposed change that goes through a human approval step and is then executed under the approving user's own SyteLine identity.
- 1
Data access
Read-only service account against IDOs and curated SQL views of estimates, orders, jobs, routings and item costs. Respects the SyteLine site and user group scoping the customer already has.
- 2
Retrieval index
Closed jobs and past estimates embedded with their part attributes, material, quantities and actual cost, stored in a vector index on the customer's own servers and refreshed on a schedule.
- 3
Model serving
An open-weight model served with vLLM or Ollama on customer GPUs or in a private cloud. No quote or cost data leaves the customer's network.
- 4
Agent and guardrails
Tool-calling layer with a fixed set of approved queries, a visible query log, and drafts only. Pricing rules from the ERP stay authoritative, the model proposes and explains.
- 5
Approval and audit
Estimator approval before any write, with a log of the prompt, data read, and edits made so a quote can be explained later.
Integration notes for your ERP team
- Read estimates, orders, jobs and items through IDOs where possible, since IDO security and site scoping are then enforced by SyteLine itself.
- Use SQL views on a read replica for heavy historical analysis, to avoid load on the application tier during quoting hours.
- Map quoted-versus-actual cost carefully: planned and actual cost fields differ between standard, average and lot costing methods.
- Write-back, if enabled, calls the same IDO methods a user would, under the approving user's identity so SyteLine audit fields stay accurate.
- Attach RFQ documents through SyteLine's document attachment mechanism so the estimate shell and source file stay linked.
- Custom user-defined fields on estimates and orders are common, so the field map is built per site rather than assumed.
Deployment options
Fully on-prem
Defense and aerospace suppliers whose quotes and drawings are controlled data
Model, index and agent run on servers beside the SyteLine database. Works air-gapped once weights are loaded.
Private cloud tenant
Mid-market manufacturers on CloudSuite Industrial who still want data isolation
Dedicated GPU instance in the customer's own cloud account, reading SyteLine through the supported integration surface.
Pilot on a replica
Teams that want to test comparable-job search before touching production
A read-only database replica and a small model validate the quote use case with no load on the live system.
Compliance and data control
How the architecture supports your obligations. Certification and accountability stay with your organisation; the design keeps the evidence straightforward.
ITAR / EAR controlled technical data
Quotes and drawings can be export-controlled. Keeping retrieval and inference inside the customer's boundary lets the customer apply their own access controls. Compliance remains the customer's obligation.
Customer NDAs and pricing confidentiality
Role-scoped retrieval prevents one customer's pricing from being surfaced when quoting another customer where policy forbids it.
SOX / internal controls on pricing
Approval, change history and a logged reasoning trail support the segregation of duties already expected around price overrides.
Data protection (GDPR and similar)
Customer contact data stays on customer infrastructure, with retention and deletion following the customer's policy.
How an engagement runs
Phase 1 . 1-2 weeks
Discovery
- -Review of current estimating process and forms
- -Inventory of custom fields and price rules
- -Selection of 3 to 5 target product families
- -Success measures agreed with the sales lead
Phase 2 . 2-3 weeks
Data and index build
- -Read-only access and curated views
- -Closed-job and estimate index
- -Quoted versus actual cost reconciliation
- -Data quality findings
Phase 3 . 3-4 weeks
Pilot with estimators
- -Comparable-job search live for a small group
- -Draft cost build-up with approval step
- -Feedback log and corrections
- -Accuracy review against recent quotes
Phase 4 . 2-3 weeks
Rollout and handover
- -Wider estimator access
- -Monitoring and query log review
- -Runbook for the customer's IT team
- -Decision on write-back scope
Questions to ask any vendor, including us
A short list that separates real Infor SyteLine AI work from a chatbot demo.
- Does the system show the query and the past jobs it used, or just a number?
- Can it be limited so it never suggests another customer's pricing where policy forbids it?
- How does it handle jobs whose actual cost was distorted by rework or a one-time event?
- What happens when routing hours in the ERP are known to be wrong?
- Which actions can it take without approval? The answer should be none.
- Can it run with no outbound internet access?
- How will we measure whether quote accuracy actually improved?
- What does it do with custom fields we added to estimates?
Frequently asked questions
Does SyteLine have built-in AI for quoting?
Infor offers AI capabilities in its cloud portfolio, and SyteLine has strong native estimating and pricing rules. For many shops the built-in pricing matrix is enough. A private AI layer earns its place when the problem is finding comparable past jobs and judging margin, which the pricing rules do not do.
Will the AI set our prices?
No. It proposes a cost build-up and flags outliers against history. Price rules in SyteLine stay authoritative, and an estimator approves every quote. The value is speed and consistency of the first draft, not autonomous pricing.
What data does it need from SyteLine?
Estimates, customer orders, jobs, routings, item and cost data, and price codes. It reads these read-only through IDOs or SQL views. Actual cost fields matter most, because they are what lets it compare quoted against real outcomes.
How accurate are the cost suggestions?
That depends on the quality of routing and actual-cost data and on how repeatable your work is. Repeat-family work with clean history does well. One-off engineered items need more estimator judgment, and the system should say when it has few comparables.
Can it run on CloudSuite Industrial in the cloud?
Yes, through the supported integration surface, with the model hosted in a private cloud tenant. Fully air-gapped operation applies to on-prem SyteLine installations.
Can it read RFQ emails and PDFs?
It can extract part numbers, quantities and dates into an estimate shell for review. Scanned or poorly formatted documents reduce accuracy, so the estimator always checks the shell before using it.
How long does a pilot take?
A focused pilot on a few product families usually runs eight to twelve weeks from discovery to estimator feedback. Data quality, not model work, is normally the longest part.
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Talk it through with an engineer who knows Infor SyteLine
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.