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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

Query any database with natural language graph queries or your native database language* with FactEngine. Your knowledge graph...your way.

Keep your existing databases and use FactEngine's unique knowledge graph technology to query them as if they were a graph database. FactEngine is in beta release now.

 

FactEngine 

 

Query your database in natural language:

Perform business language queries over your database with FactEngine's Knowledge Language. 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

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

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

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

 

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Collaboratively model in teams

Boston Enterprise is custom made for collaborative modelling in teams.

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