Most intelligent document processing (IDP) platforms promise automation, speed and accuracy, but can they explain how they actually work?
For technical leaders making high-stakes decisions, opaque systems aren’t enough. They want transparency, control and confidence that the solution they choose can accommodate edge cases, scale with their business and deliver consistent accuracy.
Affinda’s next-generation IDP is built differently. At its core is model memory, an approach that continuously improves results in real time. By combining agentic AI with a retrieval-augmented generation (RAG) system, it enables smarter, context-aware document processing that instantly learns from every interaction and applies that knowledge immediately.
This article explains Affinda’s unique approach, which is built for fast configuration, superior accuracy and the flexibility to extract data from any document in any format.
The limits of legacy IDP models
Many IDP tools, including early machine learning and OCR-based systems, were never designed to handle the complexity of today’s document workflows.
These systems are static: once trained, they cannot adapt to new document types or layout variations without heavy reconfiguration or retraining. As data needs scale, this lack of adaptability compounds the problem, turning automation into a bottleneck rather than a solution.
This is where next-generation IDP sets itself apart.
Traditional tools take months to configure and often become too costly to justify, especially when every new document type adds more work. Affinda replaces that with instant setup and a model memory approach that learns from every interaction, helping teams see value fast by avoiding long training cycles and heavy engineering effort. It moves beyond fixed models and brittle templates. Next generation IDP solutions evolve quickly to new variations as your business processes mature, allowing you to maintain straight through processing without disruption. ?
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What makes an IDP platform truly next-generation?
Next-generation IDP does more than read text and figures. It understands context, adapts to new document formats and continuously improves without extensive retraining.
A truly next-generation platform combines several things:
- Dynamic learning: Models learn instantly from every interaction, adapting their behavior without lengthy retraining.
- Context-aware extraction: They use context to interpret structure and semantics for more reliable data capture.
- Built-in validation rules: They apply business logic to highlight when something needs human attention, ensuring that machines handle the repetitive checks while people solve the real problems. For example, if a license plate number on a financed vehicle form doesn’t match the one on file in the business system, the system flags it so a team member can quickly confirm the right value before the data moves downstream.
- Scalability: They support new use cases with minimal effort, allowing teams to start small, test the impact and scale confidently as value becomes clear – all without adding engineering burden.
This foundation allows organizations to achieve straight-through processing, unlocking real efficiency gains that older solutions can’t match. Affinda’s architecture is designed to deliver this level of adaptability and precision out of the box, setting a new standard for modern document automation.
How retrieval-augmented generation works in IDP
At the heart of next-generation IDP is the ability to learn instantly, adapting to new document types and accommodating edge cases. Affinda achieves this by combining retrieval-augmented generation with agentic AI – a first in the IDP category – creating a model memory approach that bridges the gap between raw data extraction and reliable, context-aware interpretation.
Here’s how retrieval augmented generation works in practice:
- Retrieval step: When a new document is processed, the system retrieves the most relevant memories – previous corrections, examples and decisions – that match the structure or context of the current document.
- Generation step: The LLM uses this retrieved context, along with the document itself, to generate structured outputs with stronger grounding and fewer ambiguities.
- Feedback loop: When users make adjustments, those corrections are stored as new structured memories. Future documents with similar patterns automatically draw on these memories, improving behavior without requiring full model retraining.
This approach creates a smarter system that improves continuously based on real interactions, reducing manual interventions and delivering high accuracy without plateauing at 90 to 95% accuracy.
Inside Affinda’s model memory: real-time learning in action
Many IDP tools offer limited adaptability once deployed. Improving performance often means retraining the model, writing new rules or relying on manual workarounds, which drain engineering resources and slow progress.
Affinda’s next-generation IDP takes a very different approach.
Because the model memory sits at the core of our platform, any correction a user makes is stored instantly rather than disappearing into a training backlog. When a similar document appears again, the system recalls that learning, applies it automatically and avoids repeating the same mistake.
This creates a powerful cycle of continuous improvement. Accuracy improves naturally over time, without constant manual tuning or full-scale retraining projects. Over months and years, this adaptability drives results closer to true straight-through processing, giving teams the reliable, low-maintenance automation they’ve been waiting for.
Want a deeper dive? Our whitepaper walks through the frameworks, questions and benchmarks that help teams choose the right IDP platform.
Why transparency matters for enterprise adoption
For many organizations, the challenge isn’t just finding an AI-powered IDP, it’s trusting it.
Legacy vendors often offer little visibility into how decisions are made or how accuracy improves over time. Affinda’s next-generation IDP is designed differently.
By making the workings of model memory and RAG clear, you can see exactly how the platform learns, evolves and safeguards data accuracy. This transparency builds confidence in high-stakes, regulated environments where explainability is essential.
A smarter, more adaptable IDP
The future of IDP won’t be driven by static models or opaque systems. It will be shaped by systems that learn in real time, adapt effortlessly to new document formats and provide clear, explainable results.
Affinda’s IDP, built with agentic AI and model memory, sets this standard by delivering automation you can trust, understand and scale with confidence. Start your free trial and experience what next-gen IDP can do for you.
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