Integrating an LLM with your document processing workflows delivers significant operational
advantages and accelerates your path to intelligent automation.
Integrating LLMs
Combining a purpose-built intelligent document processing (IDP) platform with the flexibility of large language models (LLMs) allows you to move beyond standard data extraction. This hybrid approach enables advanced capabilities like summarization, contextual reasoning, and automated communication. By integrating an LLM of your choice with IDP, you can augment your existing document workflows, handle unstructured content with greater precision, and unlock new efficiencies—all within a secure, governed, and scalable environment.
Go beyond simple data extraction. Use LLMs to interpret extracted information, compare values against regulations, or normalize data to industry-specific codes and classifications.
Trigger intelligent follow-up actions based on document content. For example, an LLM can automatically draft a professional email to a supplier if an invoice contains discrepancies identified by the IDP platform.
Process large volumes of unstructured text by having an LLM generate concise summaries. This enables your teams to evaluate candidate profiles, review legal clauses, or analyze reports more efficiently.
Leverage the creative capabilities of LLMs for rapid prototyping and handling highly variable or unstructured content where deterministic rules may fall short.
Integrating an LLM into your ABBYY document processing workflow is a straightforward process. Our open architecture allows you to bring your own LLM (OpenAI, Google Gemini, Anthropic Claude, Mistral AI, etc.) and connect it as a tool to enhance extraction, validation, and post-processing tasks, ensuring you get the best of both worlds: the structure of IDP and the flexibility of generative AI.
Use ABBYY’s purpose-built platform to perform initial document classification, segmentation, and data extraction. This provides a structured, accurate foundation of facts from your documents.
Send the extracted, structured data—or specific segments of the document—to your chosen LLM via a pre-built connector or API call. This targeted approach minimizes token usage and cost while reducing hallucination risk.
Leverage the LLM's output for downstream tasks such as data enrichment, generating summaries, or drafting communications, all orchestrated within your automated workflow.
Find out how Ashling developed an innovative solution using ABBYY Vantage and GPT-4 Turbo to automate the processing of 30,000 lease agreements per year for a global fast-food franchise—with 82% accuracy.
This post breaks down the difference between large language models (LLMs) and small language models (SLMs), and explains why choosing the right model, paired with high-quality data, is key to unlocking the full potential of AI for your business.
When LLMs and Document AI are used together, the strengths of one compensate for the limitations of the other. Find out how in this article.
Ingest documents from multiple channels—mobile devices, email, shared folders, network scanners, and direct connections to business systems via API or pre-built connectors—ensuring seamless integration into your workflows, no matter how documents enter your organization. This flexibility empowers you to efficiently support diverse business processes, adapting to your specific needs and streamlining operations from every entry point.
The quality of document images can vary significantly due to issues like poor lighting and distortions from mobile cameras—or come with multiple auxiliary elements such as patterned backgrounds, protection marks, field markings, lines, and guides that obscure important information.
ABBYY’s AI-powered image enhancement algorithms optimize each image for accurate data extraction. The AI corrects distortions and separates text from the background, cleaning up even the most complex and visually busy documents—such as IDs, birth certificates, and forms—to achieve reliable results and high straight-through processing rates.
AI has transformed the ability to read and interpret content previously deemed impossible to process, dramatically expanding the use cases for automation. ABBYY IDP uses advanced AI-based optical character recognition (OCR) and intelligent character recognition (ICR) technologies to digitize printed and handwritten text, preparing it for further processing. These technologies are able to recognize the logical structure of the whole document, including complex elements such as tables, enabling document classification, data extraction, and high-quality export to digital formats.
Automate document classification and routing with AI classification models that analyze both text and image features through multimodal learning to recognize and organize documents. Once classified, documents are automatically assigned an AI extraction model for processing. By incorporating human-in-the-loop input, the models learn from user corrections and automatically adjust, continuously improving their performance over time.
Extract data from structured, semi-structured, or unstructured business documents using advanced AI and machine learning that mimic human understanding. ABBYY IDP reads and understands documents in over 200 languages and effortlessly handles complex tables, handwriting, checkmarks, barcodes, signatures, and more.
Automatic validation cross-checks information against databases and ensures compliance with built-in validation rules. Our low-code design approach gives you the flexibility to use pre-trained models available in the ABBYY Marketplace, tweak these ready-to-use models for the unique needs of your organization, or train custom models tailored to your specific documents.
Combine purpose-built AI with the flexibility of Large Language Models (LLMs) to enhance document workflows. This hybrid approach enables advanced summarization, contextual reasoning, and automated communication, unlocking new efficiencies in a secure and scalable environment.
Keep refining your processes through human-in-the-loop (HITL) review, which lets subject matter experts step in to manually check and correct document classes as well as extracted data through a convenient interface. This optional step is crucial when 100% accuracy is required or when a document doesn’t meet the specific validation rules established for each AI model. Each time a correction is made, the AI models improve through continuous learning and get more accurate.
The advanced quality analytics provided by ABBYY Document AI provide a clear understanding of your document processing performance and track improvements in straight-through processing rates over time. With actionable insights and tailored recommendations, you can pinpoint the root causes of problems and take effective actions to improve data extraction quality of the models for superior business outcomes within your IDP workflow.
ABBYY Document AI automatically exports data in the required format to meet your needs—whether JSON, CSV, XML, or others. The data is then sent seamlessly to your automation systems and business applications through simple REST API or pre-built connectors into your downstream processes.
Applying the wrong kind of AI to document processing—particularly for business-critical workflows—can create more problems than it solves. Get the playbook on how to combine Gen AI with Document AI for a multiplier effect.
This white paper explores the significant problems that arise from using raw, unstructured document data for LLM applications, and proposes a better way to ensure that your AI is built on a foundation of clarity and accuracy.
This article, including a demo video, uses an ABBY Vantage use case to demonstrate how these technologies can work together in a practical application, insurance claims automation.
Applying the wrong kind of AI to document processing—particularly for business-critical workflows—can create more problems than it solves. Get the playbook on how to combine Gen AI with Document AI for a multiplier effect.
This white paper explores the significant problems that arise from using raw, unstructured document data for LLM applications, and proposes a better way to ensure that your AI is built on a foundation of clarity and accuracy.
This article, including a demo video, uses an ABBY Vantage use case to demonstrate how these technologies can work together in a practical application, insurance claims automation.
Schedule a demo and see how ABBYY intelligent automation can transform the way you work—forever.
Find out how Ashling developed an innovative solution using ABBYY Vantage and GPT-4 Turbo to automate the processing of 30,000 lease agreements per year for a global fast-food franchise—with 82% accuracy.
This post breaks down the difference between large language models (LLMs) and small language models (SLMs), and explains why choosing the right model, paired with high-quality data, is key to unlocking the full potential of AI for your business.
When LLMs and Document AI are used together, the strengths of one compensate for the limitations of the other. Find out how in this article.
Find out how Ashling developed an innovative solution using ABBYY Vantage and GPT-4 Turbo to automate the processing of 30,000 lease agreements per year for a global fast-food franchise—with 82% accuracy.
This post breaks down the difference between large language models (LLMs) and small language models (SLMs), and explains why choosing the right model, paired with high-quality data, is key to unlocking the full potential of AI for your business.
When LLMs and Document AI are used together, the strengths of one compensate for the limitations of the other. Find out how in this article.
Find out how Ashling developed an innovative solution using ABBYY Vantage and GPT-4 Turbo to automate the processing of 30,000 lease agreements per year for a global fast-food franchise—with 82% accuracy.
This post breaks down the difference between large language models (LLMs) and small language models (SLMs), and explains why choosing the right model, paired with high-quality data, is key to unlocking the full potential of AI for your business.
When LLMs and Document AI are used together, the strengths of one compensate for the limitations of the other. Find out how in this article.
Applying the wrong kind of AI to document processing—particularly for business-critical workflows—can create more problems than it solves. Get the playbook on how to combine Gen AI with Document AI for a multiplier effect.
This white paper explores the significant problems that arise from using raw, unstructured document data for LLM applications, and proposes a better way to ensure that your AI is built on a foundation of clarity and accuracy.
This article, including a demo video, uses an ABBY Vantage use case to demonstrate how these technologies can work together in a practical application, insurance claims automation.
