solutions area
Chatbots & Natural Language Processing
Chatbots & NLP
Chatbots & NLP
Chatbots & NLP
Question answering retrieval
We develop systems that understand and respond accurately to user questions. By using natural language processing techniques, these systems can analyse inquiries in real-time and pull relevant answers from a knowledge base or database. This improves customer support and engagement by delivering instant, reliable responses to users.
Conversational chatbot
We use deep learning models, such as transformers, to provide profound machine understanding of natural languages and context. Making use of techniques like intent recognition and entity extraction, this chatbot can accurately interpret user inquiries and provide contextually relevant responses in a human-like manner.
Conversational chatbot
We use deep learning models, such as transformers, to provide profound machine understanding of natural languages and context. Making use of techniques like intent recognition and entity extraction, this chatbot can accurately interpret user inquiries and provide contextually relevant responses in a human-like manner.
Conversational chatbot
We use deep learning models, such as transformers, to provide profound machine understanding of natural languages and context. Making use of techniques like intent recognition and entity extraction, this chatbot can accurately interpret user inquiries and provide contextually relevant responses in a human-like manner.
Multi-agent orchestrator
We develop a customised multi-agent orchestrator that facilitates real-time collaboration between various chatbots and backend services. Using a microservices architecture, our orchestrator manages task delegation, ensuring that each agent specialises in specific areas. This setup routes complex queries to the most suitable agent, all while maintaining a cohesive conversation flow.
Multi-agent orchestrator
We develop a customised multi-agent orchestrator that facilitates real-time collaboration between various chatbots and backend services. Using a microservices architecture, our orchestrator manages task delegation, ensuring that each agent specialises in specific areas. This setup routes complex queries to the most suitable agent, all while maintaining a cohesive conversation flow.
Multi-agent orchestrator
We develop a customised multi-agent orchestrator that facilitates real-time collaboration between various chatbots and backend services. Using a microservices architecture, our orchestrator manages task delegation, ensuring that each agent specialises in specific areas. This setup routes complex queries to the most suitable agent, all while maintaining a cohesive conversation flow.
Personal assistant
We employ advanced techniques such as vector-based search and semantic indexing. By integrating natural language understanding and knowledge graphs, we ensure that the chatbot retrieves the most relevant information from extensive databases and performs multihop reasoning.
Personal assistant
We employ advanced techniques such as vector-based search and semantic indexing. By integrating natural language understanding and knowledge graphs, we ensure that the chatbot retrieves the most relevant information from extensive databases and performs multihop reasoning.
Personal assistant
We employ advanced techniques such as vector-based search and semantic indexing. By integrating natural language understanding and knowledge graphs, we ensure that the chatbot retrieves the most relevant information from extensive databases and performs multihop reasoning.
Chatbots & NLP
Chatbots & NLP
Chatbots & NLP
Name entity recognition
Named entity recognition (NER) identifies and classifies important entities in text, such as names, dates, places, and organisations. By using advanced algorithms, NER systems extract valuable information from unstructured data. It’s crucial for automating tasks like data entry, organising content, and enhancing search functions.
Relationship graph extraction
Implement sophisticated natural language processing techniques to identify and map relationships between entities within a dataset. By using algorithms like dependency parsing and coreference resolution, we analyse textual data to create dynamic graphs that illustrate connections among entities.
Relationship graph extraction
Implement sophisticated natural language processing techniques to identify and map relationships between entities within a dataset. By using algorithms like dependency parsing and coreference resolution, we analyse textual data to create dynamic graphs that illustrate connections among entities.
Relationship graph extraction
Implement sophisticated natural language processing techniques to identify and map relationships between entities within a dataset. By using algorithms like dependency parsing and coreference resolution, we analyse textual data to create dynamic graphs that illustrate connections among entities.
Entity linking
Leverage deep learning models to disambiguate and link recognized entities to relevant entries in knowledge bases. By integrating contextual embeddings and fuzzy matching techniques, we ensure that entities are accurately associated with their corresponding real-world references.
Entity linking
Leverage deep learning models to disambiguate and link recognized entities to relevant entries in knowledge bases. By integrating contextual embeddings and fuzzy matching techniques, we ensure that entities are accurately associated with their corresponding real-world references.
Entity linking
Leverage deep learning models to disambiguate and link recognized entities to relevant entries in knowledge bases. By integrating contextual embeddings and fuzzy matching techniques, we ensure that entities are accurately associated with their corresponding real-world references.
Geocoding
Deploy NLP and geospatial algorithms to convert textual location data into geographic coordinates. By applying techniques such as gazetteer matching and machine learning classification, we accurately identify and geocode entities related to locations.
Geocoding
Deploy NLP and geospatial algorithms to convert textual location data into geographic coordinates. By applying techniques such as gazetteer matching and machine learning classification, we accurately identify and geocode entities related to locations.
Geocoding
Deploy NLP and geospatial algorithms to convert textual location data into geographic coordinates. By applying techniques such as gazetteer matching and machine learning classification, we accurately identify and geocode entities related to locations.
Chatbots & NLP
Chatbots & NLP
Chatbots & NLP
Text analysis
Text analysis involves techniques for processing and examining large amounts of text to provide valuable understanding. This includes methods like sentiment analysis, topic summarisation, and keyword extraction. By using natural language processing, you can understand customer feelings, spot trends, and make informed decisions.
Text summarisation
To build text summarization tools we use advanced natural language processing techniques, including extractive and abstractive summarisation models. By utilising large language models, embeddings and other proper models, we condense lengthy documents into short summaries while keeping the essential information and context.
Text summarisation
To build text summarization tools we use advanced natural language processing techniques, including extractive and abstractive summarisation models. By utilising large language models, embeddings and other proper models, we condense lengthy documents into short summaries while keeping the essential information and context.
Text summarisation
To build text summarization tools we use advanced natural language processing techniques, including extractive and abstractive summarisation models. By utilising large language models, embeddings and other proper models, we condense lengthy documents into short summaries while keeping the essential information and context.
Keyword extraction
Automate the categorisation of your textual data using supervised machine learning techniques and modern LLMs. By training on labelled datasets and fine-tuning pre-trained models, we build systems that accurately classify text into predefined categories and tags, or even make those tags on the fly.
Keyword extraction
Automate the categorisation of your textual data using supervised machine learning techniques and modern LLMs. By training on labelled datasets and fine-tuning pre-trained models, we build systems that accurately classify text into predefined categories and tags, or even make those tags on the fly.
Keyword extraction
Automate the categorisation of your textual data using supervised machine learning techniques and modern LLMs. By training on labelled datasets and fine-tuning pre-trained models, we build systems that accurately classify text into predefined categories and tags, or even make those tags on the fly.
Sentiment analysis
Leverage natural language understanding and machine learning to evaluate and classify emotions expressed in text. By using sentiment scoring algorithms, we determine the overall sentiment—positive, negative, or neutral—of customer reviews, social media posts, and other textual content.
Sentiment analysis
Leverage natural language understanding and machine learning to evaluate and classify emotions expressed in text. By using sentiment scoring algorithms, we determine the overall sentiment—positive, negative, or neutral—of customer reviews, social media posts, and other textual content.
Sentiment analysis
Leverage natural language understanding and machine learning to evaluate and classify emotions expressed in text. By using sentiment scoring algorithms, we determine the overall sentiment—positive, negative, or neutral—of customer reviews, social media posts, and other textual content.
SEO optimization
We develop SEO optimisation tools that use text analysis techniques to enhance the visibility and ranking of online content. By employing keyword extraction, competitive analysis, and semantic search optimisation, we identify opportunities to improve content structure and relevance.
SEO optimization
We develop SEO optimisation tools that use text analysis techniques to enhance the visibility and ranking of online content. By employing keyword extraction, competitive analysis, and semantic search optimisation, we identify opportunities to improve content structure and relevance.
SEO optimization
We develop SEO optimisation tools that use text analysis techniques to enhance the visibility and ranking of online content. By employing keyword extraction, competitive analysis, and semantic search optimisation, we identify opportunities to improve content structure and relevance.
Let's talk with our CEO
Email: andrii.rohovyi@postdata.ai
Let's talk with our CEO
Email: andrii.rohovyi@postdata.ai
Let's talk with our CEO
Email: andrii.rohovyi@postdata.ai
Postdata. All right reserved. © 2024
Postdata. All right reserved. © 2024
Postdata. All right reserved. © 2024