Exclusive: ABBYY brings zero-shot document AI to Vantage. Wed, 16th Sep 2026 (Today) ABBYY is adding zero-shot document extraction to its Vantage platform as it develops a modular, multi-model architecture designed to move enterprise artificial intelligence projects beyond pilot deployments. Zero-shot release "Just this week, we're releasing zero-shot capabilities and more with Phoenix Plus inside our Vantage platform. That's being delivered today to the market," said Max Vermeir, vice-president of AI strategy at ABBYY. The capability can extract information from previously unseen document types without requiring customers to create templates or assemble labelled training data for each format. It forms part of Phoenix, ABBYY's portfolio of models optimised for document processing. Phoenix Core combines computer vision, image enhancement and technologies that identify document structure. Phoenix Plus adds generative capabilities to interpret information within that structure, including zero-shot and few-shot extraction, classification, question answering and data enrichment. ABBYY is combining the portfolio with orchestration and model-routing functions that select technologies for specific document-processing tasks. Customers can use ABBYY's models or connect models already approved and deployed in their infrastructure through a bring-your-own-model option. The company is also working to make functions across its portfolio available as individual components. Customers could then deploy selected capabilities in different products and environments without adopting an entire platform configuration. This work includes adding components to FlexiCapture, ABBYY's established document-capture product, and revising how FineReader Engine can be deployed in different environments. The roadmap also covers assisted manual-review agents, workflow enhancements and additional enrichment features for Vantage. "I don't think that's actually the question any more: is AI already in the enterprise? It absolutely is. The question that everybody is asking is: is it actually working? Is it actually producing something functional that helps your organisation, and is not just an impressive demo? Because that's a huge difference," Vermeir added. Model routing "It's not one model that rules them all. Despite what the more excitable corners of the market would like you to believe, you need a combination, a portfolio of different technologies. Generative AI models matter because documents are messy. They have ambiguity, extensive context and reasoning requirements. But the governance layer is, for me, the most important one. Reliability, auditability and cost control make the difference between a successful pilot and something that truly runs in operational production and delivers a return on investment," Vermeir said. ABBYY's approach combines deterministic technology, machine learning and probabilistic generative models. Its orchestration layer routes each task to an appropriate model while applying common validation rules, policies and oversight. The architecture addresses a practical concern for companies running high-volume processes. Using a large, general-purpose model for every document and task can produce variable results and unpredictable token costs. Specialised models can handle narrowly defined functions, while generative systems are reserved for work requiring interpretation or reasoning. The platform provides human review for exceptions and maintains traceability throughout the processing workflow. ABBYY considers these controls necessary for regulated or operationally sensitive uses, where organisations must determine how information was extracted and why an automated decision was made. According to Vermeir, operational systems require consistent results from identical inputs, integration with existing software and predictable costs. The underlying models may already be capable of handling many enterprise tasks, but their usefulness depends on access to data that accurately reflects how an organisation works. ABBYY has more than 150 specialised models covering different document types. Its broader portfolio supports combinations of smaller task-specific models, machine learning, language models and symbolic reasoning rather than relying on a single system. Document context "When I say that enterprises run on documents, it's not nostalgia for paper; it's simply a reality," Vermeir said. Contracts, invoices, insurance claims, compliance records and shipping documents contain much of the information used in business processes. Although their storage formats have changed, documents remain a primary way for people and organisations to record obligations, evidence, approvals and financial information. Many companies have automated the systems before and after document processing while retaining manual review in the middle. ABBYY argues that completing this step requires more than text extraction: systems must also recognise layouts, relationships, context and meaning. The company describes this capability as a perception layer between source documents and enterprise AI systems. It converts unstructured information into standardised operational data that software and autonomous agents can use to make decisions or trigger actions. This requirement becomes more important as organisations introduce agentic AI to plan tasks, co-ordinate workflows and act across business systems. An agent working with incomplete or incorrectly interpreted document data could spread an error through subsequent systems at greater speed and scale. ABBYY cited an insurance deployment that used document intelligence and AI-assisted workflows to extract claims information and validate it against systems of record. Exceptions went to a human-review interface, while other cases continued through the automated process. According to ABBYY, the deployment reduced processing cycle times by more than 80%. ABBYY says its technology has processed more than 200 billion documents, handles billions of pages annually and operates in more than 30 industries. It supports more than 200 languages and has more than 10,000 deployments. Open standard "DocLang is an AI-native document standard that allows you to encapsulate all of your business information in a format that is built for AI. It gives you machine-readable information, an open standard and reliable pipelines. It has governance built in, and it also creates the opportunity to reduce token consumption by between 40% and 80% simply by transforming your data into an AI-native standard," Vermeir said. ABBYY is participating in the DocLang initiative alongside IBM, NVIDIA, Red Hat and the Linux Foundation. The project aims to establish a common representation for transferring document information into AI and agentic workflows. The proposed context layer would sit between existing enterprise content and the models or agents that use it. It is intended to preserve document structure and business information in a machine-readable format, reducing the need for each organisation to build separate parsers and conversion pipelines. ABBYY plans to support DocLang exports as part of its product roadmap. It is also extending the same document-understanding components across Vantage, FlexiCapture and FineReader Engine as it works towards a more unified portfolio. The standard is being developed as an open-source project under the Linux Foundation.

Exclusive: ABBYY launches hybrid AI document models. Wed, 16th Sep 2026 (Today) ABBYY has launched Phoenix Plus, a set of generative AI models designed to work alongside its established document-processing technology, as enterprises confront the reliability, governance and cost challenges of putting AI into production. Hybrid launch Initially available through an application programming interface, Phoenix Plus forms the generative component of ABBYY's broader Phoenix portfolio. ABBYY plans to integrate the models more deeply into its Vantage document AI platform, giving customers greater control and guidance when deploying generative AI. The release reflects ABBYY's view that large language models should complement, rather than replace, deterministic technologies in business-critical document workflows. Its existing models handle image enhancement, layout analysis, parsing, optical character recognition and intelligent character recognition. Phoenix Plus adds generative capabilities for tasks requiring greater flexibility or more advanced document understanding. The models have been selected, customised and fine-tuned for document processing using proprietary datasets, model research and experience gained from processing billions of documents. "Phoenix is not just one model. It's really a portfolio of both deterministic and generative models that are integrated and optimised for document processing by us at ABBYY," said Slavena Hristova, Director of Product Marketing, ABBYY. ABBYY is also exploring smaller, domain-specific language models for particular industries and use cases. These models are intended to increase precision while reducing computational requirements and operating costs. Phoenix Plus does not require customers to standardise on a single AI architecture. ABBYY's document-processing pipeline can apply rule-based extraction, machine learning, large language models or visual language models at different stages, depending on the document and task. "It allows you to plug, at each one of these steps, the technology that actually makes sense for this part of the process, or that actually makes sense for this use case and for this document type," added Hristova. A structured invoice, for example, may require only OCR and rules-based extraction. Contracts and other highly unstructured documents may benefit more from generative models that can interpret variable language and layouts. Customers can also connect models they have already selected or fine-tuned. Production gap Generative AI can appear effective when tested on a small number of documents, but processing thousands or millions of files introduces further requirements. Operational documents vary in quality, structure and complexity, while damaged scans, unusual layouts and incomplete information create exceptions that models must handle safely. Production systems therefore need more than model inference. They may require image preprocessing, document classification, validation rules, exception handling, workflow integration and human review. Organisations must also monitor performance and maintain service levels as models and source documents change. ABBYY cited a global fund administration company that processes around one million financial documents with complex tables each year. Its internal AI team spent months trying to automate the work but failed to achieve the reliability the business required. The organisation subsequently deployed ABBYY's technology and was operating it within about a month, according to Hristova. The case reflects the build-versus-buy decision facing enterprise AI teams. Developing an internal system gives an organisation control over its technology, but also makes it responsible for the surrounding infrastructure, integrations, validation mechanisms and long-term maintenance. Generative models present a particular challenge because their outputs are probabilistic. Processing the same document more than once may not always produce an identical result. A model can also generate information that does not appear in the source, creating operational risk when extracted data informs decisions in insurance, banking, finance or other regulated sectors. These risks become more serious when automated workflows affect customers directly. Organisations may need to show where extracted information originated, reproduce processing results and maintain audit trails covering automated decisions and human intervention. Model choice ABBYY already offers a bring-your-own-model option through a prompt-based activity in its Document AI platform. It provides pre-engineered prompts, tools for testing them against customers' documents and controls for managing the output. The platform supports connections to external generative AI services, including models from OpenAI, Google and Mistral. Microsoft Foundry provides access to a wider selection of models, and ABBYY intends to expand the number of supported connections. Mistral has attracted interest from European organisations concerned about data sovereignty, Hristova said. Phoenix Plus, by comparison, is hosted by ABBYY and follows the same data privacy arrangements as the Vantage cloud service. Rather than sending an unprocessed PDF for a model to interpret independently, the platform can provide OCR-derived structure. ABBYY also plans to make its DocLang format available in Vantage. DocLang is designed to preserve more of a document's structure and context, reducing the work required from a generative model and helping to constrain its output. The orchestration layer routes each stage of a workflow to the most appropriate technology. Customers can use ABBYY's generative models, connect an external model or retain deterministic processing where it provides sufficient accuracy. Generative AI can also reduce initial configuration time. Traditional machine-learning deployments may require customers to collect and label large sets of sample documents before training models. Generative systems can begin extracting information from fewer examples, potentially shortening the time needed to add a new document type to an automated workflow. ABBYY's hybrid approach retains that flexibility while using deterministic tools for tasks where repeatability and precise extraction matter more. Generative models can then handle exceptions, unstructured content or stages that previously required human interpretation. Cost controls The economics of enterprise AI are becoming a larger part of deployment decisions as organisations limit token consumption and employees' use of AI services. A model that is economical during a proof of concept may become expensive at high document volumes, particularly if it must repeatedly interpret entire files. CPU-optimised deterministic models can cost less for routine functions such as OCR, classification and rules-based extraction. Restricting generative AI to stages where it adds measurable capability can also make processing costs more predictable. "The fact is that this approach of throwing AI and an LLM at every problem is not going to be tolerated for a really long time from now on by finance departments, because there is uncontrolled, unpredictable cost, and very often there is no need to use AI for something that can be solved with much more efficient, much more reliable, much more accurate technology, and something that is simply cheaper to run and operate," said Hristova. The cost of an internally developed document AI system extends beyond model inference. Organisations must account for infrastructure, validation, monitoring, integrations, exception management and model drift. They also assume responsibility for service-level agreements and the engineering resources needed to maintain the system. For ABBYY, the production case depends on selecting technology for each task rather than applying one model across an entire workflow. "You end up optimising the process for reliability, for accuracy, for speed instead of trying to fit the use case to the technology," added Hristova.