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 →Quarterly Report filed 2026-04-24
Material Event filed 2026-04-22
Most enterprise AI projects do not fail because the model is wrong. They fail because the data feeding the model was never trustworthy to begin with, and nothing in the pipeline told the agent to stop. This blog examines the specific ways data quality failures propagate silently through agentic systems and that trust cannot live inside an observability tool alone..For agents to act correctly at inference time, quality signals generated at ingestion need to travel into the Enterprise Context Layer, the unified foundation of business meaning, governance rules, and data health signals that agents depend on to act correctly. Without it, agents operate on assumptions they have no way to question. The post The Context Layer That AI Agents Need Most Is the One Enterprises Have Not Built Yet appeared first on Telmai .
The rise of agentic AI is exposing a critical gap in enterprise data infrastructure. Autonomous systems execute thousands of decisions per second on data they cannot verify, and when that data is unreliable, the consequences compound at machine speed. Telmai and iLink Digital are addressing this together by combining continuous data observability with enterprise scale implementation expertise, ensuring every AI workload runs on a foundation it can trust. The post Telmai and iLink Digital Partner to Bring AI-Driven Data Observability to Enterprises appeared first on Telmai .
Most engineering teams encourage AI adoption. Few rebuild around it. At Telmai, we did both and the difference is measurable. Our team went from 0% to 80–90% AI-assisted code through a deliberate three-phase shift that required rearchitecting our codebase, confronting real skepticism, and changing what we ask of our engineers entirely. This is what that journey actually looks like. The post From AI Adoption to AI-Native: How We Rebuilt Engineering at Telmai appeared first on Telmai .
Explore key announcements from FabCon and SQLCon 2026 through the lens of data quality and observability. Discover what they signal for data engineers, architects, and CDOs building on Microsoft Fabric. The post FabCon 2026 Recap: How Microsoft is turning Fabric into the control plane for trustworthy, AI‑ready data appeared first on Telmai .
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
Based on estimated brand signals. Historical tracking coming soon.
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