AI Data Extraction

Get the fields. Keep the evidence.

Useful information appears in emails, PDFs, images and long notes. An extraction pipeline returns a typed schema with source references and missing-field indicators for downstream systems.

Plan this workflow
01 / HOW IT CAN WORK

A workflow with explicit responsibilities.

AI DATA EXTRACTIONIllustrative workflow · no external actions

Source

The workflow begins with an authorized input. Useful information appears in emails, PDFs, images and long notes. An extraction pipeline returns a typed schema with source references and missing-field indicators for downstream systems.

Select any node to inspect its role.

Measure the result.
Understand the exceptions.

Measure correctness per field, missing data and rejection behavior. Schema-valid output can still be factually wrong and needs source-level validation.

What we need to design it

Bring representative inputs, the current process, the systems involved and the person responsible for the final result. We identify missing data, required API access and actions that need approval. Sensitive examples should be appropriately redacted before sharing.

What a pilot should include

A working journey, a review screen where needed, observable failure states and an evaluation against your acceptance criteria. We include the cost of reviewing and correcting output when assessing whether the pilot improves the workflow.

Safe integration

Use scoped service credentials, validated contracts and explicit authorization. Existing business systems remain the source of truth. A generated response or predicted field cannot silently overwrite a record without the validation and approval the workflow requires.

After the first release

Track the failures and unanswered questions, then improve the narrow workflow before increasing scope. Changes to prompts, models, source data or connected APIs should be evaluated because they can alter behavior even when the interface looks unchanged.

02 / UNDERLYING CAPABILITY

Generative AI Development

Build AI features that create, summarize and transform information inside a real customer or employee experience. We connect model behavior to your product requirements, editorial standards and data boundaries.

Explore the engineering
01

Is this a finished product or a solution we can commission?

This page describes a solution approach. We scope and build the implementation around your systems, permissions and requirements. No external actions run from this illustrative workflow.

02

How do we decide whether it is worth building?

Measure correctness per field, missing data and rejection behavior. Schema-valid output can still be factually wrong and needs source-level validation.

03

Can our team keep final control?

Yes. We agree review and escalation rules before implementation and make the exact proposed action visible to an authorized reviewer.

Assess the starting conditions
THE NEXT CHAPTER

Turn the use case into a working system.

Bring the business problem. We’ll work through the architecture together.

Start a conversation