AI-Powered Data Pipelines: Breaking Document Processing Bottlenecks

Jean-Pierre Pienaar
Oct 7
3 min read

Jean-Pierre Pienaar, Head of Cloud Engineering at cloudandthings.io, had the privilege of sharing insights on AI-Powered Data Pipelines and how we're breaking through traditional document processing bottlenecks using AWS Bedrock Data Automation at the AWS Community Day SA 2025. We wanted to share those insights here as well.

JP Pienaar, Head of Cloud Engineering at cloudandthings.io

Key Challenges in Traditional Data Engineering:

Format Explosion: PDFs, images, APIs, IoT streams, each requiring custom parsers.
Brittle Integrations: Weeks of dev effort per new data source or modality.
Manual Validation: Rule-based checks can't scale to millions of documents.
Late Quality Detection: Issues found downstream = costly reprocessing.
Dark Data: Unstructured silos in lakes remain inaccessible to analytics and GenAI.

Multiple tools and technologies exist today to extract information from various data formats, but building custom processes for each data type or information format slows down the pace of innovation in your downstream systems.

Couple this with the desire of organisations to adopt GenAI use cases and not be left behind, the challenge becomes quite vast.

Our Solution: AWS Native Multi-Modal Data processes

So how do we get around the time-consuming process of building custom parsers and processes for every modality and use case you might come across…. Bedrock Data Automation.

Using Bedrock Data Automation (BDA) at the core of this solution enables multi-modal data processing at scale. BDA is a multi-modal, scalable, and secure framework that enables:

Generative AI-powered extraction from documents, images, audio, and video.
Blueprints for custom prompts: normalisation, validation, summarisation, RAG population, and inference.
Modality Routing: Seamless processing across formats with no preprocessing.
Security by Default: Encrypted at rest/in transit, VPC endpoints, custom KMS keys.
Confidence Scoring: Explainability and hallucination mitigation built in.

With generative AI being non-deterministic, data processing of unstructured data types will always have the potential for poor data quality downstream. Our solution is making use of Glue DataBrew Data Quality checks to validate data before it lands in your system. This, in combination with BDA’s Confidence scoring, ensures a high-quality unstructured data processing solution at scale.

Blueprints, more than just Intelligent Document Processing:

Custom blueprints in BDA enables you to make use of generative AI to define a structure for your unstructured dataset. It allows for custom fields to be extracted from any modality and offers additional features:

Data Normalisation allows you to parse data to a predefined format or type, i.e. ensure the date extracted is represented in YYYY-MM-DD format
Data Validation enables inferred information from an unstructured dataset, i.e. are all pages of this contract signed?
Custom Inference Fields is the ability to infer data not traditionally present in the document, i.e. what is the person’s hair colour in this ID document
RAG Population enables you to very simply vectorise any modality for use in your knowledge base or custom applications

Real-World Use Cases:

Vendor Invoice Processing – Process thousands of Invoices regardless of the format or template used.
Customer Onboarding & FICA – Collect any Proof of residence from customers in any format, screenshots, documents, or scanned documents to enable seamless FICA compliance in your organisation
Knowledge Base Enablement via RAG – Process any historical unstructured data into a vector DB to enable a complete knowledge base solution for your organisation
Call Centre Insights – This solution enables you to do summarisation of call recordings, sentiment analysis, as well as load all previous datasets into a knowledge base to enhance your team’s efficiency. You are also able to extract specific fields from the call recordings, like the validation questions, as a structured dataset.

We’re enabling organisations to unlock the full value of their data, regardless of format or volume, and accelerate GenAI adoption with confidence.