Berkeley, 94704, CA,
United States
RelationalAI Technographics
Discover the latest software purchases and digital transformation initiatives being undertaken by RelationalAI and its business and technology executives. Each quarter our research team identifies on-prem and cloud applications that are being used by the 140 RelationalAI employees from the public (Press Releases, Customer References, Testimonials, Case Studies and Success Stories) and proprietary sources.
During our research, we have identified that RelationalAI has purchased the following applications: Diffbot for ML and Data Science Platforms in 2020 and the related IT decision-makers and key stakeholders.
Our database provides customer insight and contextual information on which enterprise applications and software systems RelationalAI is running and its propensity to invest more and deepen its relationship with Diffbot or identify new suppliers as part of their overall Digital and IT transformation projects to stay competitive, fend off threats from disruptive forces, or comply with internal mandates to improve overall enterprise efficiency.
We have been analyzing RelationalAI revenues, which have grown to $25.0 million in 2024, plus its IT budget and roadmap, cloud software purchases, aggregating massive amounts of data points that form the basis of our forecast assumptions for RelationalAI intention to invest in emerging technologies such as AI, Machine Learning, IoT, Blockchain, Autonomous Database or in cloud-based ERP, HCM, CRM, EPM, Procurement or Treasury applications.
AI Development
Vendor |
Previous System |
Application |
Category |
Market |
VAR/SI |
When |
Live |
Insight |
|---|---|---|---|---|---|---|---|---|
| Diffbot | Legacy | Diffbot | ML and Data Science Platforms | AI Development | n/a | 2020 | 2020 |
In 2020 RelationalAI integrated Diffbot to augment sparse retail product data and enrich its product knowledge graph within its ML and Data Science Platforms stack. The integration used Diffbot’s Product extraction and Knowledge Graph capabilities to ingest structured product entities and attributes, enabling downstream use for search, recommendations and inference across RelationalAI applications in the United States.
Diffbot’s Product extraction outputs were mapped into RelationalAI’s knowledge graph architecture, feeding the product inference pipeline and consolidating product semantics for recommendation and search models. The deployment emphasized knowledge graph augmentation and inference orchestration, with work scoped to retail product data and instrumented to support research outputs, including an accepted paper at KDD 2020. The project materially accelerated RelationalAI’s product inference pipeline, and the implementation narrative centers on ingestion, entity resolution, graph enrichment, and operationalizing Diffbot data for search and recommendation workflows.
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