Telmai provides data quality and anomaly detection solutions for AI workloads. The company offers no-code connection to data lakes and lakehouses, data validation, and ML-driven anomaly detection.

Telmai

An AI-based data quality and observability platform natively designed for open architecture.

No Sampling, no blind spots
Effortlessly process your entire data, identifying column value level issues. Our platform automatically excludes bad data from AI workloads, saving you time and resources. 

Open Architecture, design for AI workloads
Validate and monitor raw data in S3, GCS, and ADLS stored in open formats like Apache Iceberg, Delta, and Hudi.

Say "NO" to cloud cost surge 
Natively designed as a metric monitoring system, Telmai delivers high-performance and elastic scale at very low cloud cost. 

Secured, zero-copy data
Data will only stay in your cloud storage. Telmai is a fully managed service running within your VPC account.

Y Combinator

Zetta Venture Partners

Glasswing Ventures

.406 Ventures

Better Together 🤝❣

With Google's mission to organize the world's data and Telmai's commitment to ensuring reliable data, we enable users to transform vast data into trustworthy and actionable outcomes.
 
📽 Check out this video where our co-founder and CEO Mona Rakibe discusses how the synergy between Telmai's AI-driven data observability platform and Google Cloud (GCP) is a perfect fit for empowering businesses to harness data for success by:
• 🔄 Ensuring a continuous, reliable data flow with sophisticated quality workflows, such as circuit breakers and DQ binning, directly within Cloud Composer.
• 📊 Streamlining data governance by adopting both user-defined and ML-driven data quality rules, enhanced with comprehensive data health reports.

https://lnkd.in/ggibSziX

#dataobservability #genai #googlecloudplatform #datamanagement

Good data science + Bad data = Bad business results 

Unchecked data quality can derail even the most sophisticated AI models, leading to unreliable insights that will hurt your business's bottom line. 😓
The worst part? Fixing these issues is not only expensive and time-consuming, but once trust in your data-driven insights is lost, it's hard to regain.

So who's responsible for data quality? According to our 2024 State of Data Quality Survey, 43% of organizations delegate data quality duties to their Data Engineering teams. Only 14% have dedicated data quality teams, and many organizations may be potentially overlooking the need for specialized focus on data quality across departments. 

Understanding how you manage data ownership and choosing the right approach can make all the difference in keeping your data—and your data-driven implementations—on track. 🚀

Don’t let poor data hold your AI back. Check out the link in the comments to learn more! 🔗

#DataQuality #AI #MachineLearning #DataOwnership #BusinessIntelligence #BigData #TrustInData

Struggling to maintain data reliability in your AI projects? 🤔 Data quality problems can derail your efforts and quickly undermine trust in your insights.

We’re excited to partner with The Ravit Show for a brand-new Data Quality Series! 🎉 In the first episode, "𝗨𝗻𝗽𝗮𝗰𝗸𝗶𝗻𝗴 𝗗𝗮𝘁𝗮 𝗤𝘂𝗮𝗹𝗶𝘁𝘆 𝗶𝗻 𝘁𝗵𝗲 𝗔𝗴𝗲 𝗼𝗳 𝗔𝗜," our CEO and Co-Founder, Mona Rakibe, will play host alongside Ravit Jain for a live discussion. They’ll be sharing the stage with top enterprise data leaders and practitioners such as:
•Ellen Nielsen - Chief Data Officer, Top 100 Influential People in Data, USA
•Miguel Navarro - Innovator, Patented Inventor, Seasoned Digital Leader
•Appal Badi - Head of Enterprise Data Products, First Citizens Bank, Silicon Valley (Formerly)

Together, they’ll dive into crucial topics, from data reliability and observability to the impact of AI on foundational data needs. Expect practical insights based on real-world scenarios that you can implement to tackle your own data challenges. 💡

Don’t miss out—hit the link in the comments to save your spot for the live show on August 28th! 📅

#DataQuality #AI #DataReliability #GenAI #LLM #Dataobservability

Consider a financial institution that relies on an advanced AI model for real-time trading decisions. The model processes market data, client profiles, and performance metrics. On a major trade day, as new data streams in rapidly, the data team decides to retrain the model on the fly. To speed things up, they sample just 10% of the incoming data for quality checks, assuming it’s representative enough to catch any major issues.

But here’s where things could go wrong: a subtle error—a misreported market index—slips through unnoticed, triggering the AI model to make incorrect predictions. The system initiates bad trades, leading to significant financial losses within minutes. The issue goes undetected until it's too late.

The aftermath would be severe: auditing the entire pipeline to identify the root cause, cleaning, reprocessing data, and retraining models—all while stakeholder trust plummets. The cost of remediation would skyrocket, both in resources and reputation.

Could this have been avoided with full pipeline data quality checks? 🤔 What strategies do you use to ensure data quality at every stage? 🔍

#DataQuality #AI #DataDriven #DataIntegrity #TrustInData #DataManagement

🚨 Struggling to Choose the Right Data Observability Tool? You’re Not Alone! 🚨

Choosing a platform that doesn't quite fit can set you back, and that’s a risk no data-driven organization should take. 🚫 The consequences?  Stakeholders lose trust in your data-driven initiatives and dealing with downstream failures can become an exhausting and expensive ordeal. 😓

But there’s good news! 🎉 Over the past few years, we’ve interviewed numerous data practitioners, digging into their challenges and what they really need from a data observability tool. What started as a collection of internal notes and insights has evolved into something much more valuable. We’ve taken these learnings, validated them against market trends and user feedback, and compiled them into 𝗧𝗵𝗲 𝗨𝗹𝘁𝗶𝗺𝗮𝘁𝗲 𝗚𝘂𝗶𝗱𝗲 𝗧𝗼 𝗗𝗮𝘁𝗮 𝗢𝗯𝘀𝗲𝗿𝘃𝗮𝗯𝗶𝗹𝗶𝘁𝘆 𝗧𝗼𝗼𝗹𝘀. 📚

The guide covers critical areas like:
✅ Integrations with Data Lakes, Warehouses, and BI Tools
✅ Comprehensive Data Quality Reporting
✅ Defining and Managing Data Quality Rules
✅ Proactive Data Monitoring and Anomaly Detection
✅ Effective Notification and Alerting, plus API Access
✅ Remediation Strategies
✅ Scalability, Performance, and Deployment Considerations
✅ Understanding Total Cost of Ownership (TCO) and Time to Value

Don’t miss out on this essential resource! 🚀 Click on the link below and access the guide now 
https://lnkd.in/gwHPhWpx

#dataquality #dataobservability #data

"Data Pipeline Observability is just a fancy alert system" or "Data Pipeline Observability is only for big pipelines"—statements like these are sure to make heads turn in disbelief. 😲 It’s time we bust these myths wide open.

Mona Rakibe, CEO and Co-Founder of Telmai, takes these misconceptions head-on breaking down why data pipeline observability is far more than just an alert system. She highlights how data pipeline observability provides continuous oversight of your pipeline, constantly monitoring for potential disruptions and adapting workflows on the fly to maintain data quality and operational efficiency.

Curious to learn more? The full article is just a click away—find the link in the comments to know more! 🧐

#DataPipelineObservability #DataPipelines #DataQuality #Datahumour

What could go wrong if your data pipeline tests aren’t up to par? 😰 Faulty reports, broken models, as well as a lot of costly and tedious cleanup work. Without proper tests in place, small issues can snowball into big problems, impacting operational flow across the board and loss of trust with stakeholders.

Maxim Lukichev outlines three crucial methods to ensure this never happens. He covers everything from end-point validations to comprehensive pipeline-wide testing, showing how to catch issues before they become costly mistakes.

Don't let your data pipeline be a ticking time bomb. Click on the link in the comments to learn how to keep it safe and sound. 💡

#DataPipeline #DataQuality #DataTesting #DataReliability

Managing your data pipelines starting to feel like an endless game of whack-a-mole? 😟 Inefficient data pipelines can generate poor insights, costing you time and money. 🛠️ 💸

But it doesn't have to be this way. In our latest article, "The 12 Data Pipeline Metrics That Matter Most," we break down the key metrics that can turn your pipeline into a well-oiled machine. 🛠️ From monitoring performance and quality to optimizing efficiency, learn how to monitor and optimize your data pipeline effectively. 

Curious to learn more? Click the link in the comments to read the full article. 

P.S We’re now on Medium—swing by! You might find some other great reads there. 😉

Your data pipeline is cruising forward, but is your data quality strategy just trying to stay afloat? 🌊 Dark data and outdated data quality processes can lead to inefficiencies and rising cloud query costs, making it harder to stay on course. ⚠️

Telmai's Enterprise Guide to Data Health Assessment shows you how to bring your data quality strategy in line with your pipeline’s growth. 📈 Learn how to tackle dark data, reduce inefficiencies, and manage your cloud spend budget effectively. 🛠️

Don’t get stuck paddling in circles—click on the link in the comments to download the guide and discover practical strategies that you can implement to transform your data ecosystem. 🚀

#DataQuality #CloudCosts #DataStrategy #data

Does managing data freshness feel like going twelve rounds in the ring? Those dark and stale data records sure pack a punch like Mike! 🎮🥊

Outdated data can lead to flawed insights, poor decision-making, and missed opportunities. Fresh data ensures accurate analysis, effective strategies, and better business outcomes. Maxim Lukichev's latest article breaks down why relying on metadata for data freshness can be misleading and explores the different levels of freshness and their impact on data quality and business success. 📊✨

Don’t let stale data KO your business! Click the link in the comments to find out how to keep your data relevant and avoid the pitfalls of stale data. 🚀🔗

#dataquality #dataobservability

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