Self-Service Data, Finally Done Right: A Conversation with Prat Moghe

Why wait for answers your data already has?

Prat Moghe, CEO of Promethium, dives into how their solutions flip the script on data access, delivering answers in minutes, not months, with AI at the core.

You've spent your career in the trenches of enterprise data. What about Promethium convinced you it wasn't just another data platform?

I'll never forget a conversation I had with a Fortune 500 CDO who told me it took their team four weeks to answer a simple question about customer churn because the data they needed lived across 5 different systems. Four weeks for a question that would have driven immediate business action.

I've been in the data and analytics space for decades, and I've watched this same story play out over and over. Companies invest millions in modern data stacks, optimize their infrastructure, implement all the right tools — and they still can't get actionable answers fast enough. Now with AI accelerating everything, that gap is becoming a chasm.

What convinced me about Promethium was that it attacks the root architectural problem, not just the symptoms. We're not trying to be yet another data platform that persists data. We're a purpose-built platform for a new architecture that overlays existing data platforms to meet this exact moment — when AI demands real-time access to distributed data, and traditional architectures just can't deliver at scale.

"Open Data Fabric" isn't a phrase you hear that often. What's at the core of it, and why should enterprise leaders care?

Traditional data fabrics focused on data virtualization for ETL and the unification of data sources into platforms. Promethium’s Open Data Fabric is about connecting multiple data stores to the business, enabling self-service data at the scale of AI. You can think of this as a Gen2 fabric that fills the gap between data platforms and business consumption.

This architecture unifies access, context, and collaboration because agents and users need to be able to access all data regardless of where it lives. And they need the right context — which means that the system understands what "revenue" means to finance vs. sales, or how to calculate "claims" based on multiple inputs. Finally, it needs to meet the business users where they are, whether that's in Excel, a BI tool, or a conversation with an AI assistant. An Open Data Fabric brings those elements together. It gives data teams the power to respond to questions in real-time, with intelligence, accuracy, and the flexibility to integrate with any workflow. Most importantly, the fabric does not lock them into any single platform or tool, allowing organizations to mix and match with their existing ecosystem.

Speaking of “Open”, there are lots of larger players dominating the AI/data narrative, often with sweeping claims. How does Promethium position itself versus them, and why should decision-makers care about this?

The big vendors all want to pull you deeper into their ecosystem — and for CDOs, that's becoming a critical strategic decision point. Their business models depend on you migrating everything onto their single platform, which sounds compelling until you realize what that actually means.

I'm talking to CDOs who are being told they need to spend 2-3 years and millions of dollars moving all their data into one vendor's cloud to make AI work. Meanwhile, they're getting pressure from the board to show AI results this quarter, not next year.

Promethium takes a fundamentally different approach: we work with existing infrastructures and can deploy in weeks, not years. Companies no longer have to rip and replace their Snowflake environment, abandon an existing Databricks investment, or migrate off legacy Oracle servers. We connect to all of it, right where it is.

Our open architecture resonates strongly with CDOs because they know AI is too strategic to bet on a closed system. They want results now and flexibility for the future. That's exactly what we give them.

Tell our audience about Mantra, Promethium's latest Data Answer Agent. What advanced layer does it add to your 'self-service data at AI scale' slogan?

Let me paint you the before & after picture. Before Mantra, here's what happens when the business has a question: They file a ticket with a data analyst. The analyst doesn't have access to the right data, so they go to a data engineer. The engineer has to search for the data across multiple systems, get access permissions, build a pipeline, model and transform the data. After a few weeks, it's finally in a state where the analyst can work with it. They build queries, create dashboards, and surface it back to the business.

It takes weeks for the business to see anything that involves new and complex queries. And usually, it's not quite right since most questions require iterations. The question evolves, new requirements have come up, or the original ask was more exploratory than they realized. So you iterate. Again.

With Mantra, that same data analyst gets the question and can surface a first answer in minutes, not weeks, without relying on others on the team. Maybe this solves the problem. Maybe it needs some tweaking. Maybe it proves a hypothesis that they want to evolve into something more robust. But they do that typically after they've proven the value, not before.

Mantra isn't just faster — it completely changes the dynamic from reactive ticket-taking to a proactive business partnership. In fact, our customers have seen significant gains with some realizing a 90% faster turnaround on these types of ad hoc questions and an overall 5x increase in productivity.

Those are standout numbers — 90% faster turnaround, 5x productivity. How do you ensure that these aren't just one-off wins but signs of repeatable performance?

The workflow I just described — going from weeks to minutes — that's not a one-off. That's the fundamental shift we see across enterprise deployments. When you eliminate the pipeline-building bottleneck and give data teams direct access to contextualized data, you don't get incremental improvements. You get order-of-magnitude changes.

But here's what makes it repeatable: every question that gets asked and validated becomes a reusable asset. The system remembers. So when a similar question or a variant of the same question comes up in an enterprise environment, they're not starting from scratch. They're building on previous work.

Our approach also creates a compounding effect. In the first month, they might see 2x productivity gains. By month six, teams are operating at 5x their previous capacity because they've built up this library of proven, reusable data answers. It's not just about being faster; it's about being systematically smarter over time.

How do you keep the product roadmap grounded in customer realities, especially when you're also trying to push the category forward?

We stay extremely close to our users, both executives and data teams, so much of what we're building comes directly from real pain points we hear in the field. But we're also in a dynamic, fast-moving space, so you have to look ahead and decide where you have a right to play.

Promethium isn’t trying to be your next data platform. We don't want to replace a BI tool or an existing model development environment. We're the middle layer that provisions trusted data and curates answers, no matter what tools the business uses on top.

But here's the key — everything we build must be relevant at enterprise scale. That means saying no a lot. We get requests for features that might be interesting, but don't solve the fundamental problem that enterprises face at scale. Staying disciplined about that focus keeps us from becoming just another feature-heavy platform that tries to do everything and ends up doing nothing particularly well.

If Promethium were pitching to a boardroom 10 years in the future, what would the product demo look like?

Look at how far we've come in the last 10 years — from batch pipelines and static dashboards to real-time, conversational answers. Now imagine that trajectory continuing, but accelerated by AI.

I think by 2035, the distinction between asking a human data analyst and asking an AI system will be completely blurred. These systems will have such a deep understanding of the business context that they'll proactively surface insights before an individual even knows to ask the question.

Imagine walking into the office and your data system tells you, "Customer satisfaction in the Northeast region is trending down 15% based on support ticket sentiment, supply chain delays, and social media mentions. Here are three interventions we should consider immediately."

Today, we're building toward that future where data becomes truly intelligent, not just accessible, but anticipatory. Where the data infrastructure doesn't just respond to questions, but actively helps discover opportunities and risks you didn't even know existed. We're laying the foundation for that world, one data answer at a time.