About the client
FactMata is a UK-based startup that builds AI-driven tools to detect misleading or false content on the internet, offering a fact-checking and content-verification platform used by publishers, organizations, and media-monitoring services. Their mission centers on improving information transparency and reducing the spread of misinformation worldwide.
In November 2022, FactMata was acquired by Cision, a global leader in public relations and media intelligence, integrating FactMata’s content-analysis capabilities into a broader media-monitoring and communications offering. This acquisition reflects their public-standing credibility and the recognized value of their AI-based misinformation detection technology.
By combining advanced machine learning, natural language processing, and extensive data aggregation, FactMata processes large datasets of news articles, social media posts, and other public content to flag potentially false claims and provide actionable insights about information trustworthiness.
Business context
FactMata, a startup aimed to build AI systems that uphold principles of transparency and accountability, had a great mission — their services analyzed and identified likely unreliable information on the internet. Well-known investors, such as Mark Cuban, Biz Stone, and Mark Pincus, recognized this potential.
In November 2022, Factmata was acquired by Cision, one of the largest companies in the public relations services industry.
In our cooperation with Factmata, Geniusee delivered top-grade tech solutions to help this project scale by continually improving data pipelines and algorithms. AI systems were trained on massive datasets to detect signals correlated with false or misleading claims.
The mission behind our work was to apply AI responsibly and deliver real value to the digital information ecosystem.
Dhruv Ghulati
CEO at Factmata
The client faced several critical challenges with their existing recruitment infrastructure:
Obtaining large datasets to train the models
Processing data from different sources of information
Developing a user-friendly online dashboard
Addressing issues related to data quality
Data enrichment with third-party data sources
Solutions we implemented
- UX/UI design. We aimed to create and refine a user-centered design that met key business objectives and the needs of end-users. Our constant collaboration, feedback, and revisions were instrumental to the success.
- Back-end development. We developed robust data pipelines to ingest, clean, and annotate the massive datasets necessary for training AI models. These datasets contain examples of statements, claims, and other information from across the internet.
- Front-end development. The front-end development process involved building the user interface to enable users to access Factmata’s solutions. The UI provides an easy way for people to upload content for analysis, view analysis results, and receive recommendations for improving or removing potentially misleading information.
- DevOps. We developed scalable solutions that could handle up to 3 million records for a single company. In particular, it was crucial to distribute the components across different parts of the cluster to ensure efficient operation.
- Data engineering. To develop FactMata’s data engineering capabilities, our team focused on building a robust data pipeline and storage infrastructure. We aggregated data from various public data sources and preprocessed it. We optimized our resources and developed solutions with Kubeflow. As part of our data engineering operations, we also migrated processes to a new data storage system.
- Integration with an unstructured data process. We aggregated data from various public data sources, including news articles, social media posts, and forum discussions. This was done to build comprehensive datasets for training FactMata’s machine-learning models.
- Performance optimization and scalability. We tuned FactMata’s database to enhance query performance and scalability. Our team optimized source code by reducing redundant logic, caching frequently used values, and improving algorithms. That way, we improved the platform’s speed and efficiency.
- Project management. We maintained close contact with FactMata through sprint planning sessions and sprint reviews. This allowed us to gain a deep understanding of requirements and make any necessary adjustments to our development process.
Each feature was built to empower data-driven facility management and improve employee experience.
Analysis of content quality
FactMata’s algorithms analyzed the accuracy of claims and statements across the internet. By cross-referencing claims with a vast database of factual information from reputable sources, FactMata could automatically detect and flag potentially false information.
Narrative monitoring
Through this feature, we can group similar opinions to form narratives, which one can view to learn what is being said internationally about a topic or a brand. It helped identify and understand the individual opinions being shared on online platforms.
User-friendly online dashboard with detailed results
The dashboard included analytics on moderation decisions, fact-checking accuracy, and user management tools. Additionally, it offered search functionalities and integration options, ensuring efficient content moderation and fact-checking processes.
