Tiger Analytics just hit a 7 with Databricks Brickbuilder Specializations, validating our obsession with the last mile of data value. Enterprises are betting big on AI. They are building the right foundations for it. Yet data debt is still a hurdle, and they need to bridge the gap between having a data platform and running a data-driven, AI-powered business. This recognition is built on our track record of solving the three biggest gaps in the enterprise. This milestone is a thank you to our incredible teams and Databricks, and a commitment to our clients to enable them scale faster, govern better and realize the ROI of their Databricks investment.
Tiger Analytics is proud to be a launch partner for Databricks Lakebase. Lakebase is a unified engine designed to power enterprise-grade AI and ML with sub-second latency. It connects live transactional data, analytics, feature stores, and models to deliver reliable, production-ready intelligence. As a launch partner, Tiger Analytics is working with global enterprises to retire legacy operational databases, build real-time, AI-first applications, and orchestrate autonomous agentic workflows on a single, secure, and governed platform. This allows AI systems to reason, make decisions, and evolve with full contextual accuracy. This collaboration reflects a shared focus on building AI-native infrastructure that can scale from early adoption to enterprise-wide deployment while supporting real-world business needs. Read more: https://lnkd.in/gQK3TgDB
Congratulations, Anand Jha and Ganapathy Subramanian N on making it to the Snowflake 2026 Data Superheroes list for the second time in a row! Snowflake's Data Superheroes is a select global group of practitioners influencing how the AI Data Cloud is built, adopted, and advanced.
"Growth is not limited by demand but by the pace at which we transform ourselves to take up all the work coming our way." — Mahesh Kumar In a recent conversation with Sindhu Hariharan, The Hindu BusinessLine, Mahesh Kumar and Pradeep Gulipalli share how long term client partnerships, APAC expansion, and cutting-edge AI work are shaping our journey.
In this article on @CIO, Gnana Prakash, Client Partner at Tiger Analytics, reflects on why the challenge isn’t AI in BI itself, but the assumption that one generic agent can understand the entire enterprise. Drawing from real-world deployments, he outlines where copilots typically fall short: 🔸Limited understanding of sales processes, pipeline logic, and conversion rules 🔸Gaps in operational workflows, SOPs, and execution constraints 🔸Shallow context on product hierarchies, KPIs, and semantic models 🔸Missing awareness of planning cycles, supply chain realities, and business trade-offs The article makes a nuanced case for why multi-agent AI may be a more realistic path forward for BI, with specialized agents working together across domains rather than one assistant trying to do it all.
What if the biggest barrier to Net-Zero isn't technology, but the way we handle fragmented data? Today, we’re solving that by joining Snowflake for the launch of their Energy Solutions. By unifying IT, OT, and IoT data streams within the AI Data Cloud, we’re helping energy leaders see the full picture and transform their energy operations for the first time. From predicting asset failure before it happens to streamlining safety protocols, we are making AI-driven operations a practical reality.
Putting consumer understanding at the center of revenue decisions will be the #1 strategy for consumer brands in 2026. Kellanova’s RGM Navigator is a strong example of this shift. By combining advanced analytics and AI, it helps teams get real-time answers to crucial questions in pricing and promotions, like what to promote, when to act, and how to balance growth with discipline. Through their RGM Navigator, they are moving beyond standard reporting to creating a virtual analyst capable of identifying trends and explaining growth drivers automatically. Partnering with Kellanova on this journey reflects our shared goal of building systems that truly connect with the consumer experience.
Most AI experiments look impressive until they face the complexity of a live production environment. Bridging that gap requires a shift from simple automation to agentic workflows that can handle the nuance of real-world operations and manage complex deployments independently. We have spent significant time refining this with Microsoft, and achieving this Specialization with Azure & GitHub marks a key milestone in our journey. This deep technical alignment ensures that when we build AI-native pipelines, they are capable of accelerating delivery cycles without compromising the security or stability enterprise systems demand.
Shravan Pai’s session at NRF 2026 focused on breaking that cycle. Leveraging the Databricks Data Intelligence Platform, we explored how Agentic RGM fundamentally changes the physics of decision-making. The demo highlighted a shift from static modeling to active orchestration, where: ➡️Persona-aware personalization drives revenue lift. ➡️Autonomous agents run hundreds of scenarios in near real-time. ➡️Intelligent tool-calling bridges the gap between high-level revenue strategy and ground-level execution. Thanks to our partners and RGM leaders who stopped by to challenge the status quo and explore what a truly agentic future looks like.
At NRF 2026, Ujwal Joseph took the stage at the Google Cloud booth to showcase how we’re closing that gap with TrendAct. The demo showed how agentic intelligence can detect a cultural spike, validate it for commercial viability, and immediately trigger the right campaign. Powered by Google Gemini, TrendAct helps CPG and Retail teams move faster across: ✳️Product design & assortment shaping ✳️In-season forecasting and inventory decisions ✳️Personalized recommendations and search ✳️Creative and campaign activation Thanks to our partners at Google Cloud, and the retail and CPG leaders who joined the session.
