#intelligent data extraction

As data pipelines grow more complex, speed alone is no longer the goal. Stability, reproducibility, and trust matter just as much. 

That’s where IDE practices inside DataOps start to make a real difference. When development environments are tightly integrated with deployment, validation, and monitoring workflows, teams spend less time fixing breakages and more time improving outcomes. 

Organisations adopting this approach are seeing clearer handoffs between engineering and analytics, faster iteration without sacrificing governance, and fewer surprises in production. 

Good DataOps is not about tooling alone. It is about creating an environment where data work can move quickly and reliably at the same time. 

Where do your data workflows still feel fragile today?

Construction projects generate huge volumes of technical documents but most of that information is still locked in PDFs and emails. 

The challenge isn’t lack of data; it’s the time it takes to pull structured insights from it. 

This overview explores how intelligent extraction is being applied in construction workflows, especially where teams need to: 

Identify entities across unstructured documents 

Extract technical data without manual scanning 

Reduce rework caused by missed details 

Build a consistent digital trail for project decisions 

For teams exploring more efficient document workflows, the full analysis is here: https://linkly.link/2S2UY

This past month, the Infrrd team collaborated with our client, a large financial institution that has a team of about 100 people extracting data from documents manually. This customer processes a huge variety of documents from numerous sources that do not have any fixed format. Every document from every provider comes with its own unique layout and vocabulary of information. These documents arrive at the firm’s mail room where they are combined into packets of hundreds of documents then get handed-off to the data processing team. In all, the customer has millions of combinations of layouts from which the team extracts data.

Infrrd believes that a good partnership is designed to create high value for our customers, benefit both partners, and achieve a significant and meaningful business case for both partners.

Infrrd’s best-in-class intelligent document processing (IDP) helps partners build out their capabilities and deftly navigate a rapidly changing technology landscape.

Infrrd has developed connectors to partner platforms and established a presence on leading marketplaces including UI Path and Betty Blocks. Its flexible solution and adaptability have helped Infrrd and its partners together address diverse customer requirements and to generate real results for large enterprise clients.

OCR, AI OCR or Intelligent Document Processing (IDP) are solutions that extract data from documents. OCR is used for simple documents, while IDP can handle a greater variety of documents, from simple to complex, unstructured documents.

AI OCR Accuracy Trends

Accuracy is an important topic to understand when automating data extraction from documents.  Yet there is significant uncertainty around what accuracy is and what can be achieved for your use case.When it comes to accuracy, Infrrd believes the answer to “what’s my accuracy?” should be something you can bank on.Accuracy uncertainty should not be a roadblock to your success.  

Nikunj Sanghvi, Infrrd’s VP intelligent automation, is passionate about helping businesses improve their speed and efficiency. He likes being part of a team that offers tailored solutions to clients interested in optimizing and transforming their processes, and says its Infrrd’s patented technology, the ability to increase accuracy and throughput, and industry-specific integrations, that make it possible. “We help them unlock their data, so that they can use it to run their businesses smarter,” he says.

Originally published at infrrd.ai.

Rindegastos, a Chile-based cross-platform application that digitizes the expense reporting process, had a problem. It needed to process receipts from Chile, Argentina and Peru, but being able to read the data from different countries and detect which country the receipt was coming from turned out to be a tall order.

 Originally published at infrrd.ai.

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