Company Overview
About Telmai
Telmai is a Mountain View, California-based AI data observability platform — backed by $11.39 million in total funding including a $5.5 million seed round co-led by Glasswing Ventures and .406 Ventures with Zetta Venture Partners — providing data engineers and data scientists with ML-powered real-time monitoring of data quality across data lakes and warehouses in raw format at scale without data sampling, offering record-level anomaly detection, data drift monitoring, and data quality scoring for open architecture environments (Apache Iceberg, Delta Lake, Hudi) that traditional data observability tools built for structured SQL warehouses cannot adequately monitor. Founded in 2020 and generating $2.6 million in revenue in 2024 (up from $1.5M in 2023) with a 14-person team.
Business Model & Competitive Advantage
Telmai's technical approach differs from warehouse-centric data observability tools (Monte Carlo, Bigeye, Anomalo): while competitors primarily monitor data warehouse tables using SQL-based statistical profiling (detecting schema changes, distribution shifts in structured columns, freshness delays), Telmai monitors raw data files in data lakes at the record level — reading Parquet, Delta Lake, Iceberg, and Avro format files directly and applying ML models that don't require SQL transformation to detect anomalies in nested JSON structures, complex data types, and semi-structured data that raw format analytics increasingly requires. The no-sampling approach (monitoring 100% of records rather than statistical samples) enables detection of the rare-event anomalies (one-in-ten-thousand records with a corrupt field) that sampling-based approaches miss, while Telmai's distributed architecture processes lake-scale data volumes without performance degradation.
Competitive Landscape 2025–2026
In 2025, Telmai competes in the data observability, data quality monitoring, and MLOps data validation market with Monte Carlo Data (data observability, $235M raised at $1.6B valuation), Great Expectations/GX Cloud (open-source data quality, $40M raised), and dbt tests (open-source SQL data testing within dbt pipelines) for data engineering quality monitoring adoption. The data observability market has grown as data-driven organizations discover that ML models, dashboards, and analytics powered by bad data produce business decisions worse than no data — the cost of data quality failures (bad predictions, incorrect reports, compliance violations from incorrect data) exceeds the monitoring investment. Glasswing Ventures' focus on AI and data infrastructure aligns with Telmai's positioning at the ML-powered data quality intersection. The 2025 strategy focuses on growing the open data lakehouse market (Iceberg/Delta Lake on cloud storage where Databricks and Snowflake's open table format initiatives are accelerating), building the LLM training data quality monitoring use case (detecting data quality issues in datasets used for AI model training), and expanding the enterprise security and audit compliance features.
Recent Activity
View all →Enterprise data teams are leaning harder on autonomous workflows to drive business and strategic outcomes. Agentic applications ingest, transform, and act on data in real time. Yet the data powering them is rarely monitored continuously and carries no quality context at runtime. The challenge grows as data ecosystems become heterogeneous and distributed, with quality defined […] The post Bringing Context-Aware Data Observability To Unity Catalog appeared first on Telmai .
Material Event filed 2026-10-07
Key Differentiators
Emerging Innovator
Telmai is an emerging player bringing innovative solutions to the Data & Analytics market.
Frequently Asked Questions
Estimated Visibility Trend (Beta)
Simulated 8-week rolling score
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