Telmai Sees Strong Enterprise Adoption Amid Shift to Agentic AI and Lakehouse Architectures

As enterprises adopt open table formats like Apache Iceberg and Delta Lake to build composable lakehouse architectures, ensuring data reliability across distributed pipelines becomes a critical challenge. Telmai addresses this with AI-powered data quality monitoring, now trusted by organizations like PropertyGuru Group, a Fortune 200 real estate firm & a leading fintech company.

SAN FRANCISCO, July 22, 2025 /PRNewswire/ -- Telmai, a leader in data quality and observability for data lakehouses, is emerging as the data trust layer for enterprises scaling modern lakehouse architectures built on open table formats such as Apache Iceberg and Delta Lake.

A Fortune 200 global real estate leader recently implemented Telmai to address data quality gaps across a large-scale Delta Lake architecture. With over 50 million records and a multi-cloud data platform supporting business lines, Telmai enabled real-time profiling, anomaly detection, and observability, transforming their governance operations from reactive to proactive.

Following this success, Telmai has expanded adoption with new enterprise customers, including PropertyGuru and a leading fintech company. Both organizations selected Telmai to ensure reliable, AI-ready data pipelines across customer-facing applications and ML workflows, deploying Telmai within modern lakehouse environments built on open table formats such as Apache Iceberg and Delta Lake. These wins further solidify Telmai's position as the data observability and quality platform of choice for enterprises scaling on open architectures.

PropertyGuru strengthens data reliability across GCP environments with Telmai

PropertyGuru Group, Southeast Asia's leading PropTech company with number one property marketplaces in Singapore, Malaysia, Vietnam, and Thailand, adopted Telmai to strengthen trust in the data powering its customer-facing applications and ML-driven recommendation systems. PropertyGuru operates in a multi-cloud environment, leveraging Google Cloud Platform for data infrastructure and AWS for customer-facing applications. With plans to extend coverage to AWS-based systems like Athena and RDS, the data teams at PropertyGuru needed a solution that could scale observability across a diverse set of sources while ensuring consistent data quality for business-critical insights.

The organization needed to improve visibility into data anomalies and inconsistencies. Without a unified framework to monitor data health trends, engineering teams were frequently pulled in for manual DQ checks, investigation, and remediation, slowing resolution times and undermining confidence in data-driven initiatives.

PropertyGuru chose Telmai to replace manual workflows with automated, real-time observability. Telmai proactively detected anomalies and automated remediation, resulting in operational efficiency gains. Its AI-augmented data quality checks helped ensure data accuracy and maintain compliance at scale with regulatory requirements.

"Buying or selling a home is one of the most significant decisions a person can make, and our customers rely on us to provide accurate, trustworthy data. That trust depends on the reliability of the information we deliver. In practice, managing property data across multiple markets, each with its own systems, sources, and standards, is highly complex. Add in a multi-cloud infrastructure and varying levels of data quality, and it becomes easy for data inconsistencies to slip through. Telmai has become a critical partner in helping us stay ahead of those challenges. Its ability to proactively detect anomalies and alert us before problems escalate gives our team the confidence that we're delivering reliable data consistently and at scale" said Marek Tuchalski, Director of Engineering for Data at PropertyGuru Group.

Leading fintech company accelerates observability across critical data pipelines with Telmai

A leading fintech company adopted Telmai to improve the reliability and accuracy of data flowing through its enterprise systems. With data sourced from both internal platforms and third-party vendors, the team needed a scalable solution to detect anomalies early, reduce manual remediation, and maintain confidence in downstream reporting and analytics.

As the company evaluated solutions to support its shift toward AI-driven automation, a key priority was finding a scalable way to embed data reliability at the ingestion layer, without burdening engineering teams with manual rule maintenance or custom code.

Telmai stood out for its real-time, AI-augmented observability, open architecture, and native compatibility with modern data stacks, particularly Apache Kafka for event streams and open table formats like Delta Lake and Apache Iceberg.

"As we invest in AI-driven automation, trust in our data foundation becomes critical. Our vision is to make data quality proactive, embedded, and scalable, without slowing down innovation. Telmai aligns with that vision by bringing intelligent data quality and observability right where our pipelines begin," said the Director of Product Management.

During the evaluation, the team quickly onboarded Iceberg-based datasets to Telmai, configured business rules, and began detecting data issues proactively. This early success validated Telmai's ability to accelerate the development of AI-driven workflows by ensuring data quality upstream, reducing investigative effort, and increasing agility across their data pipelines.

By choosing Telmai, they are not just solving for today's needs, but they're leading the way in building a modern data architecture where data observability is intelligent, automated, and deeply embedded.

Mona Rakibe, co-founder and CEO of Telmai, noted, "As organizations adopt modern data platforms and open table formats for their AI projects, the need for real-time, high-quality data becomes mission-critical. We're proud that leading organizations are choosing Telmai to embed observability early in the pipeline, where it matters most."

About Telmai:

Telmai is a data observability platform that enables enterprise data owners to monitor and detect real-time data issues. The platform leverages AI to monitor all data passing through the data pipeline before reaching business, protecting downstream systems and analytics used for decision-making. Built for open architectures, Telmai offers seamless, no-code integrations with over 250 sources and native support for Iceberg and other open formats.

For more information, please visit Telmai or request a demo.

Contact:
Anoop Gopalam
Product Marketing & Growth @ Telmai
[email protected]

SOURCE Telmai