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The technique is field extraction, cross-source comparison, and embedding search. Production cases—origination, commerce, and system knowledge—draw the line: the model proposes; policy and people decide.

Where it already runs

Credit origination

The file is no longer assembled by hand. The model reads the document and returns fields the credit flow can use immediately.

Payslip: employer, income, and deductions.
Credit bureaus: reported obligations and behavior.
ID document: identity check against the application.
Comparison between payslip data and bureau reports.
Agreements and catalogs matched by similarity when names are not an exact match.

Commerce

A merchant catalog gets completed without rewriting every product record from scratch.

Reading a product sheet or input the merchant already has.
Sales copy written for that product—not a generic paragraph.
Product imagery when the catalog has nothing publishable.

Operational knowledge

Product questions are answered from business sources—not memorized replies.

Capability lookup: what the system does and with what evidence.
A requirements draft from the conversation, ready for engineering.
Case research using operational information, with the source in view.

How it is governed

Output for a system

The result is a field contract—not a paragraph someone has to reinterpret.

More than one model

The primary path runs on AWS Bedrock. OpenAI joins when the case needs it. The flow is not locked to a single provider.

Own semantic search

Agreements and catalogs are stored as embeddings in a vector database alongside operational data.

The business still decides

In credit, the model does not approve or deny. It clarifies the file for risk policy and for the originator.

From a manual case to the system

Pick the manual work
A document that gets typed, a comparison done in a spreadsheet, or a question that always lands on the same person.
Set the contract
Which fields come out, what is good enough, and what happens when the model is unsure: it stops and a person reviews.
Put it in the existing flow
Origination, document repository, or the commerce channel. AI does not open a parallel system.
Correct with real cases
Watch the documents that fail and adjust the prompt, catalog, or rule. A pilot is not closed on day one.

In this work, data science means making the data trustworthy before asking for a prediction: source, owner, and rule. Without that, the model only accelerates error. If the case is organizing operational information, continue withintelligent data management. If the case is keeping a change from breaking the flow, continue withquality assurance.

Talk about an AI case

AI & data science

Where it already runs

Credit origination

Commerce

Operational knowledge

How it is governed

Output for a system

More than one model

Own semantic search

The business still decides

From a manual case to the system

Pick the manual work

Set the contract

Put it in the existing flow

Correct with real cases

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