Open Architecture, AI-driven Data Observability Startup Telmai Raises Oversubscribed Seed Funding of $5.5M

SAN FRANCISCO, June 16, 2023 — Telmai announced it has raised $5.5 million in oversubscribed seed funding. The round is co-led by Glasswing Ventures and .406 Ventures, with participation from current investors, including Zetta Venture Partners. Telmai will use the new funding to expand its team and meet increased demand for its AI-driven data observability platform.

Telmai’s seed round comes on the heels of several significant milestones for the company, including:

• Partnership with Google Cloud to bring ML-driven anomaly detection and data quality monitoring to BigQuery.
• Partnership with Snowflake to empower trusted data applications and products.
• Partnership with Databricks to ensure continuous reliability of data lake houses.
• Industry Recognition in G2 Data Quality Grid Report with 5-star customer ratings.
• Recent competitive customer wins with DataStax, Clearbit, and Merkle.
• New product capabilities, including Telmai data health dashboard, custom rules, end-to-end lineage, and private cloud.
• SOC 2 Type 2 compliance.

Addressing Today’s Data Challenges

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.

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).

A Future Proof Data Observability Platform

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 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.

For example, Telmai helps Clearbit deliver accurate data to its customers, managing the data quality and freshness of 50M company records, 389M contact records, and 4.5B IP addresses from over 250 sources. Harlow Ward, CTO at Clearbit, says, “Our customers deserve the best possible data to grow their business. Telmai’s data observability enables us to show the quality of the data we deliver to our customers. We use Telmai to monitor and quality check the data that flows through our proprietary algorithms and the data we package for our customers. We chose Telmai because of its ML anomaly detection and scalable architecture. Telmai will remain essential as our data landscape changes.”

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,” said Mona Rakibe, CEO and Co-Founder of Telmai. “Today, by partnering with Glasswing Ventures and .406 Ventures, along with the continued support from our existing investors, we are excited to take the next crucial steps in our company’s growth.”

“Since first partnering with Mona and Max at the pre-seed stage, we have been struck by the team’s vision in creating a new market around making data observability both simple and accessible to data teams,” said Graham Brooks, Partner at .406. “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.”

“We invested in Telmai because they bring the best vision and highly differentiated and defensible product features to the data observability market,” said Rudina Seseri, Founder and Managing Partner at Glasswing Ventures. “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. We are thrilled to work with Telmai’s executive leadership as they execute their mission and continue to innovate and expand their footprint.”

To learn more about Telmai, please visit https://www.telm.ai or request a product demo.

Join Telmai at Big Data London, where Mona Rakibe, Telmai CoFounder, will present on Data Observability Use Cases: A Look Beyond Data Quality and Incident Management at Noon on September 20, 2023, in the Modern Data Stack theater.

About Telmai

Telmai is a data observability platform company enabling enterprise data owners to monitor and detect data issues in real-time. 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.

Source: Telmai