--- title: "Yorph AI - Agentic Data Platform" description: "Yorph AI is a pioneering data platform that combines agentic AI with modern data engineering practices, bringing enterprise-grade AI capabilities to everyone. Founded in 2025 and based in Austin, TX, Yorph empowers both technical and non-technical users to build reliable, scalable data workflows with AI-powered automation, while maintaining human control and clear explainability." category: "product-overview" tags: - "agentic data platform" - "data engineering" - "data integration" - "data visualization" - "data merging" - "data cleaning" - "AI-powered analysis" - "workflow automation" - "smart data transformations" - "pipeline scheduling" author: "Yorph AI Team" url: "https://yorph.ai" created: "2025-08-06" last_updated: "2025-10-09" source: "internal" visibility: "public" embedding_priority: "high" keywords: ["data workflow automation", "AI agents", "self-healing workflows", "data orchestration", "instant visualizations", "AI powered analysis"] company: "Yorph AI" headquarters: "Austin, TX" founded: 2025 industry: "Artificial Intelligence / Data Platform" language: "en" related_posts: - "you-know-what-you-need-now-build-it-ai-for-analysts" - "ai-powered-data-for-product-managers" - "security-at-yorph-agentic-ai" - "multi-agent-systems-abstraction-or-overkill" - "real-key-to-ai-driven-data-engineering" - "lessons-learned-building-reliable-multi-agent-systems" --- # Yorph AI - Agentic Data Platform ## Section: About [About Us](https://yorph.ai/about-us): Learn more about who we are and our company background Yorph is a pioneering data platform that combines the power and flexibility of agentic AI with the reliability and scalability of modern, state-of-the-art data engineering practices. Our team has a proven track record building enterprise-grade AI agents and solving complex data challenges at scale. Now, we're focused on making these capabilities accessible to everyone—so more people can work smarter with data. Founded in 2025, based in Austin, TX, and with team members around the globe, we've set out to build the next generation of tools that empower organizations and individuals to harness the power of their data. At Yorph, we believe in the power of AI to revolutionize data engineering—but we also recognize the complexities involved. What we've learned is that the real key to AI-driven data engineering lies not in what the AI does, but in what it doesn't do. The critical factor is, and always will be, the human user. We're building a solution that blends AI with strong validation, clear explainability, and complete user control. Yorph will empower non-technical users and technical users alike to become curators of their data workflows—equipped with the tools to make processes more efficient, scalable, and reliable. ## Section: Our Users [See How Yorph Works for Your Team](https://yorph.ai/#our-users) Yorph serves diverse data professionals across industries: ### Analysts **From Data Wrangling to Strategic Insights** - Yorph helps automate repetitive data workflows with AI-powered pipelines that self-heal when source systems change. Transform raw data into analysis-ready datasets while you focus on strategic analysis and game-changing insights. Whether you're a product analyst, a HR analyst, an Operations analyst, a Financial analyst, a Business analyst - if you're wrangling datasets, Yorph is for you. ### Product Managers **Stop Waiting on Engineering for Data** - Build your own data pipelines without writing code. Get accurate product metrics, automated cohort analysis, and funnel insights – all without engineering tickets. Make data-driven decisions at the speed of conversation. ### Analytics Managers **Scale Analytics Without an Engineering Team** - Democratize data pipeline creation across your organization. Empower business teams with self-service analytics while maintaining governance and security controls. Scale your data capabilities without massive infrastructure investment. ### Business Leaders **From Data Requests to Data-Driven Decisions** - No more waiting weeks for your data team to deliver insights. Yorph empowers your organization with self-service analytics, enabling faster decision-making and competitive advantage. ### Data Scientists **From Data Janitor to Model Builder** - Automate feature engineering pipelines and create consistent, model-ready datasets. Standardize data transformations across your team and accelerate the path from prototype to production. Focus on insights, not data wrangling. ### Data Engineers **From Pipeline Maintenance to Platform Building** - Generate production-ready pipelines that follow best practices automatically. Enable business teams to build simple workflows themselves while you focus on complex architecture. Reduce maintenance burden with self-healing, monitored data flows. ## Section: Core Features [Explore Yorph Features](https://yorph.ai/features) ### Integration Across Data Sources - **Diverse Data Sources**: Connect to virtually any type of data storage system - databases, spreadsheets, cloud services, APIs, and file systems - **Unifying Multiple Data Sources**: Combine data from different systems into a single, coherent view that works as if it came from one source - **Automatic Schema Detection**: Yorph automatically understands the structure and format of your data without manual setup ### Customizable and Repeatable Transformations - **Reusable Transformation Templates**: Save your data processing steps as templates that can be applied to new data automatically - **Version Controlled Logic**: Keep track of all changes to your data processing rules with automatic versioning and change history - **Custom Code Availability**: Write your own data processing logic when pre-built options don't meet your specific needs ### Scaling, Scheduling, and Validation - **Auto-Scaling Infrastructure**: Process massive amounts of data automatically, with computing power that adjusts based on your workload - **Pipeline Orchestration**: Schedule and coordinate complex data processing workflows that run automatically at whatever frequency you need - **Data Reliability at Scale**: Built-in safety that ensures your data processing runs reliably through quality checks, retry, and fallback logic ## Section: Why Yorph [Why Yorph](https://yorph.ai/#why-yorph): Understand the benefits of our platform ### Increased Efficiency & Data Reliability Reduced number of hours spent on data tasks, empowering data teams to innovate and not just maintain infrastructure. ### Competitive Advantage Reduced time to market with new business use cases through faster data pipeline development. ### Lower Spend Eliminating hidden data engineering costs, reduced tool costs, and reduced spending by scaling data teams efficiently. ## Thought Leadership & Insights [Blog Posts](https://yorph.ai/blogs): Read our latest insights and thought leadership ### Section: Thought leadership and Insights --- title: "You Know What You Need—Now Build It: AI for Analysts" author: "Yorph AI Team" published: "2025-08-14" tags: ["analysts", "data pipelines", "AI automation", "business intelligence", "self-service analytics"] summary: "Empowering analysts to break free from data engineering bottlenecks by building their own AI-powered pipelines. This post explores how modern AI can serve as a personal data engineering mentor, enabling analysts to take control of their data timeline and iterate at the speed of thought." url: "https://yorph.ai/blogs/you-know-what-you-need-now-build-it-ai-for-analysts" --- **You Know What You Need—Now Build It: AI for Analysts** - By Yorph (August 2025) Empowering analysts to break free from data engineering bottlenecks by building their own AI-powered pipelines. This post explores how modern AI can serve as a personal data engineering mentor, enabling analysts to take control of their data timeline and iterate at the speed of thought. --- title: "Stop Waiting on Engineering: AI-Powered Data for Product Managers" author: "Yorph AI Team" published: "2025-09-10" tags: ["product managers", "self-service analytics", "data independence", "product metrics", "AI-powered insights"] summary: "How product managers can build their own data pipelines without writing code or waiting on engineering tickets. This post shows how AI enables PMs to get accurate product metrics, automated cohort analysis, and funnel insights at the speed of conversation." url: "https://yorph.ai/blogs/ai-powered-data-for-product-managers" --- **Stop Waiting on Engineering: AI-Powered Data for Product Managers** - By Yorph (September 2025) How product managers can build their own data pipelines without writing code or waiting on engineering tickets. This post shows how AI enables PMs to get accurate product metrics, automated cohort analysis, and funnel insights at the speed of conversation. --- title: "Security at Yorph: Agentic AI designed for Real-World data" author: "Yorph AI Team" published: "2025-07-18" tags: ["security", "data retention", "no data for training", "agentic AI"] summary: "Yorph AI takes a security-first approach to data systems with zero data retention by default, transparent agent behavior, and strict user control.**Your data is never used to train models**—ensuring privacy, explainability, and trust are foundational, not optional." url: "https://yorph.ai/blogs/88bc4fc1-3b3e-4ada-a103-987809f0d6d2" --- **Security at Yorph: Agentic AI designed for Real-World data** - By Yorph (July 2025) Yorph AI takes a security-first approach to data systems with zero data retention by default, transparent agent behavior, and strict user control. **Your data is never used to train models**—ensuring privacy, explainability, and trust are foundational, not optional. --- title: "Multi-Agent Systems: Useful Abstraction or Overkill?" author: "Yorph AI Team" published: "2025-06-15" tags: ["multi-agent systems", "AI architecture", "system design", "complexity"] summary: "This post examines whether multi-agent systems are a meaningful architectural evolution or just unnecessary complexity.It argues that in the right contexts—like specialization, modular deployment, and scoped control—multi-agent setups move beyond prompt chaining and become a scalable foundation." url: "https://yorph.ai/blogs/multi-agent-systems" --- **Multi-Agent Systems: Useful Abstraction or Overkill?** - By Yorph (June 2025) This post examines whether multi-agent systems are a meaningful architectural evolution or just unnecessary complexity. It argues that in the right contexts—like specialization, modular deployment, and scoped control—multi-agent setups move beyond prompt chaining and become a scalable foundation. --- title: "The Real Key to AI-Driven Data Engineering - It's Not What AI Does, But What It Doesn't Do" author: "Yorph AI Team" published: "2025-05-20" tags: ["AI limitations", "data engineering", "human-in-the-loop", "AI boundaries"] summary: "This post examines whether multi-agent systems are a meaningful architectural evolution or just unnecessary complexity. It argues that in the right contexts—like specialization, modular deployment, and scoped control—multi-agent setups move beyond prompt chaining and become a scalable foundation." url: "https://yorph.ai/blogs/ai-data-engineering-boundaries" --- **The Real Key to AI-Driven Data Engineering - It's Not What AI Does, But What It Doesn't Do** - By Yorph (May 2025) This post explores why successful AI in data engineering depends less on automation and more on human oversight, explainability, and control. It argues that true value comes not from what AI can do—but from knowing where to hold it back in the face of open-ended, sensitive, and high-scale data challenges. --- title: "Lessons Learned Building Reliable Multi-Agent Systems" author: "Yorph AI Team" published: "2025-10-27" tags: ["multi-agent systems", "AI architecture", "production AI", "agent frameworks", "domain knowledge"] summary: "Key lessons from building a production-ready agentic data platform. This post explores the importance of domain knowledge, architecture, balancing deterministic code with LLM capabilities, and leveraging modern agent frameworks for reliable multi-agent systems." url: "https://yorph.ai/blogs/lessons-learned-building-reliable-multi-agent-systems" --- **Lessons Learned Building Reliable Multi-Agent Systems** - By Yorph (October 2025) We are getting ready to launch our agentic data platform and wanted to share what we think are the most important things we've learned. Turns out that building a reliable agentic system is largely about good engineering fundamentals and clear written communication. Key lessons include: - **Domain knowledge is your differentiator**: Whether it's tools, evals, or fine-tuning, your agent's domain knowledge sets you apart from being just a wrapper around an LLM - **Architecture matters**: The difference between a flashy demo and a reliable product comes down to how agents are structured, their tools, callbacks, and context management - **Balance deterministic code and LLM "magic"**: A good production system finds the middle ground between letting the LLM cook and making sure it doesn't burn down the kitchen - **Use frameworks, don't rebuild them**: Stand on the shoulders of fast-evolving Agent frameworks like Google's ADK Related articles: Multi-Agent Systems: Useful Abstraction or Overkill?, The Real Key to AI-Driven Data Engineering, and You Know What You Need, Now Build It: AI for Analysts ## Section: Get Started - Join the [Waitlist](https://yorph.ai) to try Yorph before public release - Learn more at [yorph.ai](https://yorph.ai) - Contact our team at [queries@yorph.ai](mailto:queries@yorph.ai)