Artificial Intelligence Search, Deep Learning and Machine Comprehension

The core of our technology is based on Question Answering. There are many ways machine understanding of data can help companies. From the rise of semantic search that powers smart devices to semantic search technology for the enterprise.

Main Technology

Question and Answering [QA]

A direct answer to questions

Question and Answering (QA) uses a combination of language manipulation and search techniques to find the exact information you’re looking for and offer a direct answer to questions posed in a natural language. It is based on a closed Domain (topic-specific) questions.

Information Retrieval [IR]

Keyword-based Search Engine

Information Retrieval allows for data, in various forms, to be organised for easy access and indexed for quick retrieval. Search decides what content, and in what form you see whenever you enter a query.

Deep Learning

Machines find patterns from big data

Deep Learning is a subset of machine learning where artificial neural networks, algorithms inspired by the human brain, learn from large amounts of data to perform complex tasks based on patterns.

Semantic Search

Understand context and meaning

Semantic Search attempts to interpret the user's intent in a context, instead of keyword matching alone, by understanding entities and relationships within the content. Topics and concepts are linked and related information can be suggested.

Machine Reading Comprehension [MRC]

Gather information from text

Machine Reading Comprehension is the ability for computers to read and understand the unstructured text and then answer questions about it without the need to build an ontology set or any data formating.

Natural Language Understanding [NLU]

From meaning to actionable data

Natural Language Understanding tries to deduce what questions mean, regardless of the way they are expressed, allowing users to interact with the computer using natural sentences.

NLU provides optimal answers to user questions by utilizing various NLP technologies (POS, NER, Domain Classification, Intent Analysis, etc.) and deep learning technologies that perform Context Management to exchange context-based conversations and offer answers based on user-specific sessions and history.


Multiple ways to ask

Paraphrasing allows search results to include words not directly used in the query. It recognizes that there may be multiple ways to ask a question, that all refer to the same answer.

Common Terms

Artificial Intelligence (AI)

AI is a broad family of technologies that allow new possibilities for gathering, analyzing, and understanding data by letting machines handle complex processes that emulate how humans think.

Big Data

A considerably large volume of both structured and unstructured data so big it is difficult to process using traditional database and software techniques.

Vectorizing Technology

Deep Learning can interpret Big Data and find relationships between sentences and phrases with the same meaning in large amounts of text. It then converts all context information of a text expressed a dense vector form that shows context proximity.

Insight Engine

A combination of Semantic Search and Deep Learning techniques applied to Big Data to offer insights into company operations. It creates rich indexes by processing natural language into a set of related results linked by semantic relationships which are then retrieved using probabilistic relevance.

AI-Based Solutions

Leading companies now use AI-based solutions to refine information-heavy processes, automate manual time-intensive tasks, give teams information to make better decisions, and speed time to market.

Domain Agnostic and Language Agnostic

Transferring learning technology to different industry uses based on a single system architecture.

High accuracy experiences through the application of AI-Based Technology