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FactEngine solves 5 business problems when considering a knowledge graph for your organisation. FactEngine lets you:

  1. Develop your enterprise model in business language. Your data lake becomes a semantic knowledge graph;
  2. View your enterprise model in different conceptual modelling languages (e.g. semantic model, object-role model, graph or relational);
  3. Create the database from the enterprise model. Generate programming code from your conceptual model;
  4. Work with multiple data lakes/databases that comprise your knowledge graph;
  5. Query your data lake with semantic graph queries. Work with multiple query languages;

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Your Knowledge Graph, Your Way

With FactEngine you query any database with graph queries or your native database language*. Your knowledge graph...your way.

Viev's unique knowledge graph technology lets you keep your existing databases and query them as if they were a graph database. FactEngine is in beta release now.




Query your database in natural language:

 FactEngine Knowledge Language lets you perform business language queries over your database. Natural language queries are graph queries. Write graph queries over your database Read More

FactEngine Easy

Natural Language Queries over your Knowledge Graph



Make your Data Lake a Multi-model Knowledge Graph

With Viev's unique multi-model conceptual modelling, your database can be viewed as a graph or relational database. The choice is yours. Minimise overhead by minimising the number of databases that you need to work with. Leverage off existing techology in your current architecture. Generate data defintion script for your database from inside Boston, then use FactEngine to query your database.

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FactEngine is in beta release. Contact the FactEngine team for a demonstration.

* FactEngine uses one query language and an adaptor is required for each database type.


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