Data observability insurtech Telmai has raised $5.5 mn in oversubscribed seed funding. The round is co-led by Glasswing Ventures and .406 Ventures, with participation from current investors, including Zetta Venture Partners.
Enterprises face significant challenges in understanding, monitoring, and maintaining their data ecosystems’ quality, reliability, and accuracy.
Addressing these market pain points is crucial for enterprises to fully leverage their data assets, drive informed decision-making, and maintain a competitive edge in the data-driven economy.
Founded in 2020, Telmai delivers a data observability platform to identify record value level data quality issues and anomalies at their source before data is ingested into data warehouses and AI models.
The platform leverages AI to monitor all data passing through the data pipeline before entering the data warehouse, protecting downstream systems and analytics used for decision-making. Telmai’s real-time architecture supports anomaly detection closest to data sources and works over complex data types with native support for nested and multi-valued attributes.
Telmai is led by co-founders Mona Rakibe, an entrepreneur with over 15 years of experience launching cloud products at Oracle, Dell EMC, and Reltio, and Max Lukichev, an experienced tech and data science leader and the former head of SignalFx Platform engineering at Splunk.
These enterprise data veterans’ work at Reltio and SignalFX/Splunk laid the groundwork for their understanding of the industry’s data pain points and how to architect the Telmai platform for scale.
We built Telmai using a high-scale Spark architecture allowing it to handle the growing volume, velocity, and variety of data in modern enterprises – what our customers call future-proof.
Mona Rakibe, CEO and Co-Founder of Telmai
Today, most businesses run on a hybrid data architecture, using a combination of legacy and modern data systems spread across structured, semi-structured, and event-streaming data sources, delta lakes, and cloud data warehouses.
This complex environment requires a scalable data observability platform that can detect data issues across large volumes of diverse data at marginal cost. This requirement is even more critical as the industry adopts generative AI and Large Language Models (LLMs).
To solve these issues, Telmai delivers the first and only data observability platform to identify record value level data quality issues and anomalies at their source before data is ingested into data warehouses and AI models.
Telmai has proven to win against market leaders because of its superior architecture, allowing the platform to observe any data in an ever-growing data ecosystem – and so making it future-proof
Graham Brooks, Partner at .406
Telmai uses ML to enable a low-code, no-code interface that automatically identifies issues for structured, semi-structured, and streaming sources and predicts future outcomes. This accelerates time to value for data teams for discovering data reliability issues across any source, supporting the data ecosystem’s current and future state – a revolutionary approach compared to existing solutions available in the market.
Telmai uses ML to enable a low-code, no-code interface that automatically identifies issues for structured, semi-structured, and streaming sources and predicts future outcomes.
By partnering with Glasswing Ventures and .406 Ventures, along with the continued support from existing investors, Telmai takes the next crucial steps in company’s growth.
Telmai is building the first, best, and only scalable data observability solution that enables data owners to monitor, detect, and remediate data issues in real-time.
