Optimize your data for generative AI

We pride ourselves on offering strategic collaboration, alongside access to cutting-edge technology. We'll equip your business with advanced tools for document processing and data analysis. This will position you as a leader in your industry and future-proof your business against new challenges.

We use intelligent document processing to transform any document into a digital asset, with high accuracy and speed. Our technology ensures text from scans, images, PDFs, and other documents are converted into readable data. This facilitates streamlined processing and accurate interpretation of information.

Our NLP technology enables us to extract meaningful and contextual information from text-based content. NLP is a crucial tool for organizing unstructured data into actionable insights. With its capabilities, you'll be able to:

• Conduct advanced text analysis
• Evaluate sentiments
• Identify key entities

ABBYY Vantage combines NLP with RAG and other technologies such as OCR to offer comprehensive and relevant insights, beyond just document data.

We're skilled in developing tailored Al solutions to address your specific business needs. Whether you're facing new challenges or want to optimize your processes, our custom Al models are designed to empower your business.

RAG ensures LLMs retrieve information from accurate and relevant knowledge sources. LLMs are intelligent AI tools, but a crucial drawback is they may provide outdated information by drawing from static training data.

As a result, responses from conventional language generation models might be too generic or even inaccurate. RAG gives enterprises more confidence and control over generated outputs and the response generation process.

There are three key benefits of RAG:

1. Relevant information

RAG provides current and reliable sources to LLMs, ensuring users receive the latest information.

2. Improved user confidence

RAG allows for source attribution, citations, and references. This increases users' confidence in generated responses.

3. Cost-effective training

RAG is a more affordable alternative to retraining a foundation model, making generative AI technology accessible.

Retrieval-augmented generation (RAG) and semantic search offer different approaches to information retrieval and generation. RAG combines language generation models with information retrieval techniques. It finds and integrates external data into large language models (LLMs) to improve response quality. In contrast, semantic search scans extensive databases to retrieve precise information. It accurately maps queries to relevant documents and returns specific text.

In summary, RAG prioritizes response generation from retrieved data, while semantic search focuses on delivering semantically relevant passages.

You'll receive ongoing support from your initial consultation and beyond. We draw on our extensive AI and machine learning knowledge to partner with you on your journey. With our partner ecosystem, designed to guide our customers through digital transformation, we can ensure smooth AI integration into your business processes.