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Understanding texts in documents

It has been a while since the subject of Artificial Intelligence left the academic world and arrived in the corporate world, solutions of all kinds, from Big Data to Chat Bots are already a reality. And now we brought to Brazil what was missing for companies, the result of advances in Natural Language Processing (NLP) and computer vision, allowing to interpret documents with the same efficiency as a person, coupled with the machine learning resource guarantees a powerful solution for understanding texts in your company.

Exclusive technology for document automation

Hyper-Learning

Thanks to advanced neural language modeling, the user himself can train our solution using only 10% of the examples required by other AI systems.

Accuracy and efficiency

Achieve an accuracy equal to or greater than that of humans in tasks such as document classification and extraction of information in unstructured and semi-structured texts.

Evolves with the user

Learn from interactions with business users over time, allowing you to refine document classification and information extraction.

No Code

It has an easy interface designed to be managed by the business user himself, who can take advantage of the document processing power without AI specialists.

Business benefits

Speed in handling documents ensuring a better customer experience.

Understanding of various types of documents, from tables and simple texts, to more complex and unstructured documents.

Rapid deployment and low TCO (performed by business users, not AI specialists).

Fewer errors, faster even in large volumes of documents.

Multilingual support is an indispensable factor when dealing with texts in a global economy.

The security of your business growth, facing the risks of increasing manual processes.

The next generation in document automation

Solution developed as an evolution of Natural Language Processing (NLP) and OCR, focused on Deep Learning (DL) and Computer Vision (CV), the result of all the research carried out in Neural Language modeling for Layout Recognition. The result is a solution capable of processing all types of documents – plain texts, tables, forms or unstructured documents.

Comparison with other technologies

Natural Language Processing
(Simple Technology)
  • Depends on keywords and rules created by AI specialists (Requires large amounts of data for training)
  • Handles simple and homogeneous text documents
  • Extracts information usually without interpretation
  • Struggle to work with tables and forms
  • Expensive and laborious implementation
  • Requires maintenance by AI specialists
Intelligent OCR
(Limited technology)
  • Recognizes simple letters and data, does not understand the context and does not make decisions
  • Copies only text fragments from forms and tables
  • Language dependent
  • Does not interpret the text
  • Depends on models
  • Requires expert knowledge and continuous model updates
  • Intensive maintenance process involving specialists
R1.RTA
(Based on NLP, DL e CV)
  • Faster and more accurate decision making
  • Extract information from documents according to the context
  • Agnostic in terms of language and RPA technology
  • Understands your documents regardless of format (plain text, tables or forms)
  • Deployment by the business user (no AI knowledge required)
  • Maintenance by users themselves through self-learning

Growtec exclusive representation

Superior in technology

Understanding the context in 2 dimensions

A technology that works with Deep Neural network resources with Computational Linguistics and Computer Vision algorithms. Technology that imitates the way of the human being with documents, considers the textual and graphic aspects before finding the results. Enabling accurate semantic analysis for information extraction.

Language model with layout recognition (LAMBERT)

While all language models used today are one-dimensional and do not see the structure of the text, Our layout-aware models deal with real business documents in a mix of semi-structured and unstructured text.