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CNPS × FASTGPT

Turn inconsistent part numbers into structured data

FastGPT describes a workflow that interprets component part numbers, extracts fields and connects the structured output to an enterprise database. No numerical accuracy claim is reproduced here.

南京商络电子 · Manufacturing

The published reference

FastGPT describes a workflow that interprets component part numbers, extracts fields and connects the structured output to an enterprise database. No numerical accuracy claim is reproduced here.

Read the original FastGPT case (Chinese)

Reviewed September 3, 2026. Reported results have not been independently audited by CNPS. This is not a customer project delivered by CNPS.

Reference pattern

Structured fields — part-number interpretation and database workflow

CNPS pilot concept

How to evaluate a similar workflow

Use synthetic or approved RFQs to extract the original part number, requested quantity and missing information. Match only against approved product data; a person confirms substitutions and pricing.

What to measure

Field-level correctness, missing-field detection, traceability to the input and review time.

What we agree before starting

The data and users in scope, access controls, the review process, integration dependencies and acceptance samples. Your written proposal defines deliverables, costs and timing.

Explore this pilot approach

Discuss your workflow

Tell us what happens today, the systems involved and where your team loses time. Bring a process owner and non-confidential examples.

Discuss your workflow

CNPS is a FastGPT partner. Platform licensing and implementation services are defined in your written proposal. Reference cases are published by FastGPT; CNPS has not independently audited the reported results.

Let’s start with your real-world challenge.

Tell us what you need to improve, where you work and when you want to begin.

Start a conversation