Cerevox is a no-code data observability pipeline that ensures the integrity of knowledge bases for Large Language Model (LLM) agents by employing AI-powered anomaly detection and natural language templates for data validation. This technology prevents data contamination and hallucinations, enabling fintech companies to transition from proof-of-concept to reliable AI solutions with a 100% data quality guarantee.
Funding
$4.3M raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
CFNSPVSCFounders
Product
Problem
Large Language Model (LLM) agents are prone to inaccuracies and hallucinations due to contaminated or improperly structured data in their knowledge bases. Ensuring data integrity is a significant challenge when transitioning LLM-based solutions from proof-of-concept to production, particularly in industries requiring high reliability.
Solution
Cerevox offers a no-code data observability pipeline designed to maintain the integrity of knowledge bases used by LLM agents. The platform employs AI-powered anomaly detection and natural language templates to filter, parse, and validate incoming data, preventing data exceptions from contaminating the knowledge base. By ensuring only properly structured data is used, Cerevox aims to eliminate hallucinations and improve the accuracy of LLM agent responses. Data that does not meet template requirements is held on the platform, allowing users to define exception templates to recapture and reformat the data.
Target Audience
The primary target audience includes enterprises, particularly those in the fintech industry, that are developing and deploying LLM agents for critical applications and require high data quality and reliability.
Features
- AI-powered anomaly detection to identify and block data exceptions.
- Natural language templates for defining data filters, parsing rules, validation checks, and data destinations.
- No-code configuration, enabling non-engineers to define and manage data quality rules.
- Data exception handling to track and recapture data in proper formats.
- 100% Data Quality Guarantee, ensuring only validated data enters the knowledge base.
- Integrates with on-premise, hybrid, and cloud-based knowledge bases.