Systems | Development | Analytics | API | Testing

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.

Everyone can build an AI agent today. The hard part isn't getting an agent to answer a question or complete a demo. It's deploying one that employees trust, security teams approve, and operations teams can manage at scale. That's where many AI projects stall. As organizations move beyond experimentation, the conversation shifts from prompt engineering to production readiness. Can the agent safely access business data? Can you evaluate changes before deployment? Can you understand why it made a decision?

Document-centric workflows have been difficult to automate and required human intervention. Attempts to automate document handling often failed or did not scale, because legacy intelligent document processing (IDP) systems were fragile. They often required manually retraining models on dozens of documents just to identify specific fields—only to repeat the process whenever a format changes. The result was a costly cycle of maintenance and manual data entry.

Since its inception, agentic AI has felt like a distant aspiration. Today, agents are here, and enterprise adoption is accelerating. Gartner predicts that by 2028, the average global Fortune 500 enterprise will have more than 150,000 AI agents in use, up from fewer than 15 in 2025. Agents arrive with incredible, broad intelligence, but lack the knowledge of your operating model: your customers, policies, approvals, exceptions, business rules, systems, and operational history.

Your users increasingly work through AI assistants. When they ask an agent to check a case status, analyze last quarter's metrics, or kick off an approval workflow, that agent needs to access your enterprise systems. Enabling that connection is the core challenge of AI agent integration: giving AI assistants the ability to discover, understand, and safely interact with business applications and data on behalf of users.

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.