Case Studies | SaaS B2B

Intelligent document processing, from proof of concept to production

Intelligent Document Processing

About

A SaaS B2B business working with Cloud Combinator on AWS. The client is anonymised at their request.

Challenge

The challenge had three focus areas.

Proving the approach in the real world

The proof of concept had shown that automated document processing was viable, but a proof of concept is not a product. The first focus was to understand what the experiment had proven, where its limits were, and what would need to change to run it reliably at production volumes.

Choosing the right extraction approach

Documents vary widely in layout and quality, and there is more than one credible AWS route to extracting their content. We needed to weigh a managed document extraction service against a large language model approach so that the choice fit the client's document types and accuracy needs rather than the other way round.

A dependable path to production

Moving from a working demo to a service the business depends on introduces new requirements around scale, cost, resilience and ongoing operation. The final focus was a clear, staged route that the client could follow with confidence.

Solution

In the recommended design, documents land in Amazon S3 and trigger an automated processing flow. Amazon Textract reads structured and semi structured documents to pull out text, key value pairs and tables, giving a reliable machine readable version of each document.

Changes

The engagement gave the client a clear, grounded route from proof of concept to production, with the AWS building blocks and the trade offs behind each decision set out in plain terms. The outcomes below describe the intended shape of the production service and are projected until the build is delivered and measured.

  • Proof of concept assessed — We reviewed what the initial experiment had demonstrated and identified what needed to change to run it dependably at scale.
  • Extraction approach recommended — We set out where Amazon Textract fits and where an Amazon Bedrock large language model adds value, so the technology matches the client's document types.
  • Production path defined — The client left the engagement with a recommended AWS architecture and a staged route to a production service.
  • Funding identified — The engagement was scoped so that an AWS fund request could support the next phase of delivery.
  • Handover — The client's team retained a clear understanding of the design decisions and the reasoning behind them, ready to take the build forward.

With a validated approach and a defined path to production, the client is positioned to move document processing off manual effort and onto a scalable AWS foundation, ready to grow as document volumes and use cases expand.

AWS Stack

Amazon Textract

For automated extraction of text, key value pairs and tables from documents.

Amazon Bedrock

For large language model interpretation and shaping of extracted content.

Amazon S3

For durable document storage and to trigger the processing flow.

AWS Lambda

For serverless orchestration of the processing steps.

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