Interact. Ingest. Integrate. Imagine. Innovate.


Build mind-maps of new ideas from open data sources


Create, organize and link proprietary data with open knowledge


Connect facts across disparate domains and silos


Partner around data and expertise across multiple organizations


Example use-case: Intelligence augmentation with literature

Target customers: Students and researchers


  • Software-as-a-service
  • Data
    • Open source public data
    • 6 month update cadence
    • Size less than 4 GB
  • Limited interface
  • A Knowledge Browser Interface
  • Container hosted data and deployed software


Example use-case: Hypothesis design from public content

Target customers: Research Scientists 


  • Software-as-a-service 
  • Data:
    • Open source public data updated daily
    • Size 32 GB
    • Data integration assistance
  • Context Mind Mapper
  • Query Builder
  • Programmable APIs
  • Interface + access to document DOIs


Example use-case: Enterprise-wide discovery engine

Target customers: CIOs, CTOs, CDOs                             


  • Software/Platform-as-a-service
  • Data:
    • Public data updated daily 
    • Private data
    • Size: Up to 100 TBs
  • Programmable APIs
  • Interface + access to document DOIs
  • Admin interface
  • Content creation interface
  • Data Curation and Ontology integration
  • Hosted Data Management


Example use-case: Inter-organizational innovation engine

Target customers: CIOs, CTOs, CDOs                      


  • Everything that the “Universe” product provides PLUS
    • Size: Up to 512 TB
    • Multiple data-sources (10+)
    • Multiple applications
    • Key author, Influencer
    • Organization discovery
  • Container deployment
  • Graph building
  • Multi-container launch interfaces

Competitive Differentiation

Although the space of knowledge discovery is very crowded, we believe that we have a uniquely differentiated offering for our customers.

ConceptualEyes is different from other approaches in the following ways:

(i) Our link prediction algorithms can hypothesize potential associations that are neither obvious nor explicit – i.e., it makes educated guesses about an association as opposed to simply retrieving a pre-recorded association.

(ii) Our uber platform learns on the fly by repeatedly making mistakes on noisy data– i.e., The parallel nature of the query running on a shared-memory architecture evaluates several thousand answers in the time it takes for other architectures to retrieve one single result.

(iii) Our powerful engine is a no-index exhaustive-searching divergent thinker – i.e., learns structure and saliency automatically from the data to produce a salient result-set without the bias of model-fitting seen with supervised learning products. Every query to ConceptualEyes reasons through the entire dataset before an answer is generated.