Use case
Quote preparation
From request to quote draft in the ERP: positions extracted and matched, availability and price rules checked, open points flagged. Sales releases.
01The problem
What the work looks like today
A request for quote arrives as an email with a PDF or a spreadsheet. Someone extracts positions, matches them to articles, checks availability and price rules, builds the quote in the ERP and sends it. For configured products or long position lists that takes hours, and the customer waits days.
02Failure modes
Where time and quality are lost
Three patterns we see in almost every operation of this kind.
Quotes go out days after the competitor's
Position-matching errors surface after the order
Price rules are applied from memory
03The agent
What the agent does, step by step
Extracts the positions from the incoming request, matches them to your article master and configuration rules, checks availability and applicable price lists, creates the draft quote in the ERP and summarises the open points: unclear positions, missing data, margin below the floor.
04Human checkpoint
Where a person decides
Sales releases every quote. The agent never sends. Discounts beyond the rule, special terms and strategic accounts are flagged to the account owner before the draft is complete.
05Control set
What is enforced and logged
Price rules and discount floors are enforced in the workflow, not suggested. Every quote draft shows which rule set and price list it used. Write access to the ERP is limited to draft status.
06Measurement
What the monthly report shows
Four numbers, against the baseline from the assessment.
Request-to-quote time
Share of positions matched automatically
Quote error rate after order
Hit rate on quotes sent within 24 hours
Questions
Common questions
Does the AI agent send quotes on its own?
Which data does AI quote preparation need?
Is it worth it for engineered-to-order requests?
Where to go next
Service
Workflow Build
This workflow in production in 8 to 12 weeks, with its control set.
Service
Workflow Assessment
First check whether this workflow is the right first one for you.
Guide
The quoting process in manufacturing: steps, KPIs and AI agents
The quoting process in manufacturing has eight steps from request to follow-up. An AI agent takes over reading, matching and checking, while engineering and sales decide on feasibility, price and release.
Guide
AI agent cost: what you pay for the build, operation and model usage
An AI agent costs as much as the workflow it works in. Once you know the cost blocks from integration to operation and have measured the value per case, you can set a build budget that holds up.
Next step
Strategy call
Clarify whether this workflow fits your priority.