Semantic AI Data Platform

Overcome limitations in enterprise data warehousing, data integration and federation, knowledge management, and business analytics. Our tools and algorithms transform your existing data and content into powerful knowledge systems by combining artificial and human intelligence.

Our Promise

Experts in Semantic AI

We have extensive experience in the field of Semantic AI, combining machine learning, with natural language processing and knowledge graph technologies. Our experts have processed billion of data points for SME's and large organisations alike.

Enabling strategic decision-making

Semantic search combines data from multiple sources such as web, documents, email and social media, linking structured with unstructured data to create 360-degree views to reveal information that organisation would not find otherwise.

Easy to integrate

Our team of in-house consultants will help you to get the most of your data very quickly. We are able to integrate our solutions with your systems and ways of working utilising our APIs.

Turning data into insights


Semantic AI enables enterprises and users the powerful capabilities to return valuable results from contextual meaning of terms. Our approach starts with the definition of the ontology for the client’s domain, providing means for the heterogenous data sources integrations, data validations and data capabilities. Semantic fetches results that match the meaning of a user query instead of focusing exclusively on the exact words and phrases. If an organisation would like to understand their IT ecosystem with the term "computer", the semantic search will also consider related concepts such as "laptop", "PC", "TV", "phones", and so on.

The Semantic AI Data Platform supports different data adapters to investigate data anomalies and location related suggestions.

The platform provides the capability to use rule templates in order to support:

  • Unification of data (data across different departments and silos to manage information across multiple assets),
  • Themes of interest of the customer (inferred from the Relational DB),
  • Fraud detection - based on historical information of data transactions and locations,
  • Green delivery suggestions – based on the location information, and previous customer preferences,
  • Public events of interest – based on the public information from internet,
  • etc.


Data validations & data defaulting

Inferred knowledge

Heterogeneous data sources

Developed ontology

Internet data sources

Probabilistic reasoning

Forecasted models

Our Approach

A structured and controlled process is an important criteria for a goal-oriented result. Our Semantic AI Data Platform links your business objects and content extracting value from your data to make it actionable decisions.

A structured process - from data to insights.

Strategy creation


The first step is to thoroughly understand your business, business goals, and what you would like to get out of the data. We will look at internal as well as external data sources that may be required in order to derive the full picture. This stage gives us the foundation for moving forward with just the right recommendations for the design and development stage for your Semantic AI Data Platform.



Project specification is a backbone of every project as it contains the complete description of a the functionality and purpose. It will describe how the AI should evaluate data and what information are of value. ewasoft will create a project specification which will also include a list of expected results, wireframes and other related info. Furthermore, specifications will help our developers in order to satisfy all your requirements.

Collection of data


In this step we will collect data from internal as well as external sources. For this, certain crawlers and APIs need to be created. Furthermore, the data will be structured into a database.



We build graph based recommendations that make sophisticated pairings of content through contextual and collaborative filtering. Key entities will be tagged to obtain further enriched metadata. Tagged content and metadata prove to be very valuable for knowledge management, which will in turn help to derive actionable insights for your business purpose.

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