About Advaya Intalytics

Advaya Intalytics was founded with a simple belief: organizations make better decisions when they can trust their data.

We help businesses modernize analytics platforms, improve data quality, design single source of truth, integrate fragmented systems, and build scalable reporting and decision-support capabilities.

Our experience spans financial services, banking, insurance, pharmaceuticals, and healthcare. We help organizations move from fragmented data to systems they can rely on for day-to-day decisions.

Engagements are led directly by a single person, start to finish. For scopes that need more hands than one person, we bring in vetted specialists under direct oversight — you deal with one accountable lead throughout, not a rotating bench.

The same approach — architecture and data quality first — increasingly extends to AI-enabled systems. See Current Builds for what that looks like in practice.

What We Believe

Business Outcomes First

Technology should enable better decisions, not create complexity

Trust Through Transparency

Reliable analytics begins with trusted, well-governed data

Practical Solutions

We focus on sustainable solutions that balance business value, cost, and long-term maintainability.

Our Leadership

Abhijeet Badrayani

Founder & Data Analytics Architect

Abhijeet helps organizations solve complex data challenges across finance, banking, insurance, and healthcare.

His expertise spans analytics architecture, data modernization, and decision-support systems. He has led enterprise reporting and data quality initiatives, creating single sources of truth in complex environments.

He is also actively building two products -

WisdomOS and LetStock, extending this architecture background into AI implementation.

Ragini Bhat

Co-Founder & Operations Director

Ragini oversees business operations, governance, compliance, and delivery enablement.

With a background in content development, media, and editing, she focuses on execution, process discipline, stakeholder management, and organizational effectiveness.

She also brings entrepreneurial experience from independently managing her own product initiatives. She has built products like SmartPlay Studio - binders for toddlers on various topics and engagement levels.

How Engagements Are Delivered

Specialist capacity available when a scope needs it:

· data engineering · QA & testing

· cloud platforms · software development

Engagements are led personally from kickoff to delivery — there's a single point of contact for every question, decision, and deliverable. For scopes that need additional specialist capacity (data engineering, QA, cloud platforms, software development), we bring in vetted professionals under our direct oversight. You deal with one accountable lead, not a rotating bench.

From BI Architecture to AI Implementation

AI implementation depends on the same foundation as enterprise BI: clean, governed data and a well-designed architecture underneath.

The overlap is direct, not aspirational:

Semantic modeling (Denodo) → designing the retrieval/context layer for RAG and agentic systems
Data quality frameworks → the layer that determines whether AI outputs can be trusted
Integrating fragmented legacy systems → integrating LLMs into a client's existing, messy data estate
Performance optimization at enterprise scale → production AI systems that hold up under real load

This is the same architectural discipline applied to a newer layer, not a separate practice bolted on.

See Current Builds for live examples of this in progress.

WHat this looks like in practice

End-to-end data strategy, from ingestion to reporting
Deep domain expertise in regulated industries — BFSI and pharma
Architectures designed for maintainability, not just initial delivery
Data quality and trusted reporting treated as the foundation, not an afterthought