Anonymous unmarked manufacturing enterprise · Pre-sale scheme and quotations

A Evidence level

Datawhale FDE Case 100 | No. 17: AI collaboration for non-standard manufacturing presales and quotation

Publisher: Datawhale FDE100 · Official case collection PDF · Direct sources at the case level

Claim origin: Disclosure by the author of the case · Independent verification: No · Accessed: 2026-09-19

Evidence level measures whether a source can be located and reviewed; it does not mean vendor-reported claims were independently audited.

Standard case | Useful reference with remaining gaps 8 / 12

Business context1 / 2

Transformation workflow1 / 2

Technical workflow2 / 2

Human roles & governance1 / 2

Measured outcomes1 / 2

Source traceability2 / 2

Remaining gaps: Business context, Transformation workflow, Human roles & governance, Measured outcomes

The key to this case is not the single-point use of AI, but the re-establishment of “pre-sale programmes and quotations” as an enforceable, verifiable production stream with boundaries of human responsibility.

Business problem

Pre-sale quotations relied heavily on engineers to understand non-target requirements and find similar items in historical programmes.

Solution

AI reads client requirements, retrieves historical programmes, configurations and basis of quotations, and generates a first draft of the proposal/offer.

Technical architecture & production workflow

Step 01

SOP / History Case / Expert Interview

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Step 02

Document Parsing, Cutting and Metadata Tags

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Step 03

Embedding / semantic index

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Step 04

Retrieving similar cases and rules based on current questions

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Step 05

LLM is evidence-based advice and attachment

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Step 06

• Rewrite the knowledge base of the new experience

Key technology & infrastructure components

Knowledge base/RAGEmbedding/vector searchPermissions and metadataLLMExpert review

Human roles & accountability

Sales and engineers are responsible for the final technological boundaries, prices and commitments.

FDE delivery actions

"When will the staff come to see the teacher?"
Interviews with experts to convert tacit judgment into searchable cases and rules
Design knowledge particles, labels, versions and privileges instead of simply uploading documents
Recall rate/Application of answers using real questions
Keep feeding back feedback, wrong answers and new cases.

Reusable delivery patterns

HF, high-cost, verifiable narrow-flow selection
Steps that can be validated with a certainty tool to prevent model self-assessment
Retain manual responsibility for high-risk actions
For each manual amendment to follow-up searchable context/rules

Business outcomes & delivery results

Increased efficiency of outputs from the first draft of the programme; accurate values not disclosed in public information.

Evidence boundaries & verification notes

Public information confirming business processes; technical components are abstract architecture based on public description
The source can be directly located in the case; the value remains disclosed by the source and does not represent an independent audit.

Primary source: Datawhale FDE Case 100 | No. 17: AI collaboration for non-standard manufacturing presales and quotation ↗ Additional sources: Datawhale FDE100 case webpage | No. 17 ↗ Additional sources: Secondary collation or aggregation source used in the old version ↗