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Most of what lands in an enterprise inbox isn't a clean, high-resolution PDF. It is a faxed invoice with coffee stains, a mortgage pay stub photocopied six times, a bill of lading stamped across the important fields, a 120 DPI scan from a twenty-year-old multifunction printer. Astera ReportMiner's intelligent character recognition is built for the real-world scanned document quality. For most enterprises, these degraded documents aren't edge cases. They make up the majority of the inbound pipeline.

A faster, efficient, more capable Astera with same licensing cost Astera 11.2 is available now for every product on the platform: Centerprise, ReportMiner, EDI Connect. High-volume jobs process up to 2x faster, extraction templates are generated under five seconds with AI-powered Auto-Generate Layout, and Dataflows can run a full retrieval-augmented generation (RAG) pipeline without a separate tool. The release also simplifies deployment, and adds email-driven workflow triggers.

Establishing a data warehousing system that meets all your business intelligence targets is by no means an easy task. It traditionally involves profiling source systems, designing a dimensional model by hand, writing the DDL to deploy it, building the load pipelines, and scheduling them to run, work that can take weeks. Astera's manual, step-by-step approach to this is covered in Building a Data Warehouse – A Step by Step Approach.

If you've spent any time researching accounts payable automation, you've probably run into the term RPA — robotic process automation. It's one of the oldest and most widely deployed forms of business automation, and it still plays a real role in AP departments today. But it's also frequently confused with newer, AI-powered approaches to invoice processing. This guide breaks down what RPA actually does in accounts payable, where it holds up, and where it runs out of road.

TL;DR: Fiserv platforms generate fixed-width.rpt files where every field sits at an exact character position. Astera ReportMiner maps those positions through a visual template editor, extracts the data, validates it against your business rules, and writes it directly to Excel, CSV, or 200+ other destinations. One template handles every future instance of the same report type, and the full pipeline runs unattended on a schedule.

Mainframe reports are one of the oldest and most persistent data extraction challenges in enterprise IT. They are generated by COBOL programs, printed by JES2/JES3 spoolers, and exported as fixed-width text files from IBM i Series, z/OS, AS/400, and similar systems. They power critical operations in banking, insurance, government, healthcare, and manufacturing. They also look nothing like the documents that modern AI extraction tools are designed for.

If you work in banking operations, you know what a green bar report is. You probably have a stack of them arriving every morning: end-of-day balancing reports, settlement summaries, general ledger extracts, and transaction logs that run hundreds or thousands of pages. These reports are printed in fixed-width text with alternating green and white bands so someone with a highlighter can trace a number across 132 columns without losing their place.

Every AP team eventually asks the same question: not whether to automate - as PYMNTS reports, 78% of CFOs now see AI as central to accounts payable - but which platform actually fits their ERP, invoice volume, and team. We compared 10 AI-powered AP automation platforms on capability, integrations, and real user ratings, so you can skip the demo marathon and go straight to a shortlist.

In June 2026, the highest-grossing law firm in the world committed $500 million to build its own AI platform. The firm put more than 180 engineers and data scientists and over 250 of its lawyers on the effort. It chose to build because general-purpose tools could not execute their transactions or reason over their massive institutional knowledge. That is the bill for a first-class AI product built from scratch.

In 2024, two of the largest data platform companies, each with billions in revenue and dedicated AI research teams, invested in building their own foundation models. One spent roughly $10 million training a 132-billion parameter model on 3,072 NVIDIA H100 GPUs. The other released a 480-billion parameter model optimized for enterprise tasks like SQL generation and code. Both achieved strong results within their compute class.