Promethium Wants to Make Self Service Data Work at AI Scale

(Shutterstock AI Image)

AI keeps getting smarter, but most companies are still waiting on data. No matter how powerful the technology gets, it’s the fragmented and slow-moving data that continues to drag things down. Now, with AI agents starting to play a bigger role in day-to-day work, the pressure to fix that problem is growing.

Stepping into that gap, data management startup Promethium has rolled out a major update to its Instant Data Fabric platform, which is designed specifically to support agentic AI and deliver true self-service data access at enterprise scale.

As part of the launch, Promethium is also rolling out Mantra, a new Data Answer Agent that lets users ask questions in plain language and get trusted and context-rich answers in return. Mantra connects to cloud, on-prem, and SaaS data sources without needing to move or reshape anything. It’s currently in private preview, and teams can now sign up for early access through Promethium’s website.

The latest version of the platform is designed to work with existing infrastructure and avoid the need for duplicating data or building new pipelines. It connects directly to where data already resides, whether in cloud platforms, internal systems, or SaaS applications, and makes it accessible in real time.

That kind of access is becoming increasingly important, not just for people trying to make decisions, but for AI agents that need to query data on their own and return useful insights. The problem is that most enterprise systems still rely on ETL processes that can take weeks to build and are costly to maintain.

Promethium says it removes that bottleneck by eliminating the need for traditional ETL altogether. Instead, its platform provides unified access across data sources while preserving security and control. It also focuses on context. The built-in 360° Context Engine brings together technical metadata and business logic to help users get answers that are more aligned with how the organization actually works.

Those answers can be saved and reused as “Data Answers”, which are packaged responses that can be shared, embedded into tools, or pulled into AI workflows without rebuilding anything underneath.

Promethium says these Data Answers aren’t just for one-time use. They can be saved, shared, published, or plugged into other tools, APIs, or even AI agents, without having to change existing workflows. The platform also includes detailed access controls, so teams can move faster without giving up security or compliance.

(Credits:promethium.ai)

Industry veteran Sanjeev Mohan sees Promethium’s move as part of a broader shift toward more flexible and accessible data infrastructure. Organizations are under growing pressure to connect people and systems to trusted data more quickly, without adding complexity.

“As the complexity of enterprise data landscapes grows, data teams face mounting pressure to deliver timely, trustworthy insights,” said Mohan. “A new architecture that enables open, agentic access to distributed data without adding friction is emerging. By emphasizing automation, context, and self-service, Promethium’s approach empowers data teams to shift from reactive support to strategic impact. It’s a foundational change in how data is delivered and consumed in the age of AI.”

Promethium’s leadership echoes that view, framing the platform update as a practical response to the changing role of data in AI-driven organizations.

“AI is transforming how decisions get made, but most data architectures weren’t built to keep up,” said Prat Moghe, CEO of Promethium. “With the latest version of our Instant Data Fabric and the launch of the Data Answer Agent Mantra, we’re giving data teams a new superpower: the ability to deliver trusted, contextual answers on demand. It’s the fastest, most open way to scale self-service data for the age of AI.”

Promethium was founded in 2018 with a clear goal: to take the friction out of working with data. The team believed that getting insights shouldn’t require deep technical skills or weeks of setup. Instead, they focused on building tools that let people work with data as it is, without having to move it around or rebuild everything from scratch.

With its latest rollout, the company is looking to show how that vision can scale. As more organizations test Promethium’s tools inside complex data ecosystems, the challenge ahead will be proving that simpler access can hold up under the weight of real-world complexity.

Related Items 

ETL vs ELT for Telemetry Data: Technical Approaches and Practical Tradeoffs

DataPelago Unveils Universal Engine to Unite Big Data, Advanced Analytics, and AI Workloads

AI Doesn’t Have To Be That Hard, Fivetran CEO Says