Affinda’s platform offers powerful and adaptable tools for accurate and efficient information extraction. The platform transforms information from unstructured or semi-structured documents into structured data.

Our Approach to Information Extraction

Affinda’s Information Extraction models achieve world-class document processing through a unique combination of AI techniques:

• Proprietary Reading Order Algorithms – This algorithm captures word sequences in visually rich documents in a way that aligns with human comprehension. This ensures that text is processed in the same order a human would read it, leading to more accurate extractions.
• Use of Large Language Models – Selects and utilizes the best-performing LLMs for each task, ensuring optimal results across different document types and structures.
• Model Memory with Real-Time Learning – Uses a retrieval-augmented generation (RAG) system to enable continuous improvement. Corrections made in one document are instantly applied to future extractions, eliminating recurring errors without requiring extensive retraining. See Model Memory for more information.
• Fingerprinting Algorithm – Identifies similar documents in Model Memory and provides relevant examples to the model, ensuring highly accurate data extraction and reducing errors.

This unique combination enables Affinda to build high-performing models quickly while continuously improving over time.

Benefits of Our Approach

Achieving "good enough" (e.g. ~80-90% accuracy) is not enough for mission-critical document processing. Affinda bridges the gap to "excellent" (99%+ accuracy) through our approach, reducing manual work and ensuring top-tier performance.

• No Extensive Model Training Required – Unlike traditional ML models that require hundreds of training samples, Affinda learns dynamically and applies corrections in real-time. New, high-performing models can be created in a matter of minutes, not weeks. See Creating a new Extraction Model for more information.
• Higher Accuracy, Less Manual Work – Moving from 95% to 99% accuracy reduces errors by 80%, cutting down the need for human intervention significantly.
• More Intelligent Than Static LLMs – Unlike generic large language models that rely on fixed prompts and lack continuous learning, Affinda actively applies nuanced learning from past interactions to make better decisions.