Kyvos Insights provides a semantic layer for enterprise AI and business intelligence, giving AI systems consistent business context around data definitions, relationships, rules, and governance. The platform also improves the performance and economics of enterprise analytics as data volumes and AI workloads grow. Pulse 2.0 interviewed Kyvos Insights Senior Director of Technology Pratik Jain to learn more about making AI analytics reliable at scale, how semantic layers are evolving for AI agents and LLMs, and what enterprises should consider when building their AI data architecture.
Pratik Jain’s Background And Interest In Reliable AI Analytics
When asked what drew him to the problem of making AI analytics reliable at scale and how that challenge has evolved as AI has become more deeply embedded in the enterprise, Jain explained:
Early in my career, the challenge was helping people get answers from data quickly. Today, it’s helping AI systems get the right answers consistently. Those sound similar, but they’re very different problems.
Traditional analytics had a human in the loop. If dashboard numbers didn’t look right, someone questioned it. AI changes that dynamic. An agent can retrieve information, reason over it and trigger an action in seconds. Reliability becomes critical.
What’s interesting is that the models themselves aren’t usually the biggest source of error. They’re remarkably good at language and reasoning.
A model can know exactly what “revenue” means but still get the answer wrong, because it doesn’t see how revenue ties to regions, or products, or returns. That’s the real context, the way everything fits together, not just what each piece means.
People inside the company understand those nuances instinctively. AI doesn’t unless it’s given the right context.
That’s the problem I’ve become increasingly interested in: how to help models understand how a business actually works.
I think that’s the next major challenge for the industry and it’s one we’re solving at Kyvos.
What Kyvos Does
When asked how he would describe Kyvos to someone encountering the company for the first time, Jain said:
Kyvos is a semantic layer for enterprise AI and BI.
Simply put, we give AI the context it needs to understand a business, what the data means, the relationships and rules behind it.
We keep that context in one place and make it available consistently across AI systems and analytics applications, with governance built in.
Kyvos also keeps performance fast and costs under control across all these systems, even as enterprise data grows.
And this is already working in production. Our customers are seeing AI accuracy go past 95%, token usage drop by more than 60% and cloud compute costs fall by 50%, even on billions of rows.
How Semantic Layers Are Changing For AI
When asked what has fundamentally changed about semantic layers now that AI agents and LLMs have become part of the enterprise data stack, Jain explained:
You’re right, semantic layers have been around for a long time. What’s changed is where they fit and what’s expected of them.
In the BI world, the semantic layer had a fairly well-defined job. It made business metrics available consistently across reports and dashboards.
The consumers were mostly BI tools and the queries they answered were fairly well understood.
AI is much less predictable. Every prompt is different and the volume is on a completely different scale.
That means the semantic layer has to do more than provide consistent metrics. It has to make business context available in a way AI can use, no matter how the question is asked or which application is asking it.
The economics have changed too.
AI is expensive to run at scale. Every prompt consumes tokens, and every query uses compute.
So, the semantic layer has become just as important for efficiency as it is for accuracy.
That’s probably the biggest shift. It’s no longer supporting analytics alone. It’s become foundational to how enterprise AI operates.
Addressing Contextually Wrong AI Answers
When asked how Kyvos addresses situations where AI produces statistically plausible but contextually incorrect answers around business metrics and definitions, Jain said:
Yes, and that’s because the failure is so quiet.
A model will promptly return a number when asked for net sales. Nothing about the answer looks wrong.
However, what if the model summed the raw data without applying the rules for that business or considering the discounts, returns and regional adjustments?
The answer in these cases is plausible but wrong. A person might catch that. An AI system probably won’t.
The real issue is that data rarely tells the whole story.
Every organization knows how it operates, how different parts of the business relate to each other and how information should be interpreted.
AI simply doesn’t. It hallucinates, fills in the gaps on its own and that’s where these quiet errors come from.
Kyvos takes the guesswork out of it.
Instead of expecting the model to figure out how the business works, the semantic layer provides that context.
What Enterprises Should Consider When Evaluating Semantic Layers
When asked what enterprises should be asking when evaluating native semantic layers versus universal approaches such as Kyvos, Jain explained:
I think most evaluations focus on what a product can do today. I’d spend more time asking what happens as the organization changes.
Will this still work if we introduce more AI applications next year? If another team adopts a different BI tool? Does the business context live in one place or does every new system have to recreate it?
I’d also look closely at economics.
AI has a very different cost profile from traditional analytics. If every request has to rebuild context before it can answer a question, costs rise much faster than most teams expect.
You might not notice it in a pilot, but you certainly will in production.
I’d want to know if this is still going to make sense five years from now. Will I still think this was the right architectural decision?
Enterprise AI is changing quickly. The foundation you build today should be able to evolve with it, not force you to start over every time the next model or application comes along.
And finally, I’d ask how much confidence the platform gives you.
When AI produces an answer, can you understand why? Can you trace it back to data?
Those become important questions once AI starts influencing real business decisions.
