The new startup stack: From data pipelines to agentic AI systems

The next generation of startups won’t simply use AI tools; they’ll be built around AI from day one. As businesses generate unprecedented volumes of data, the real competitive advantage is shifting from collecting information to turning it into fast, automated decisions across products, operations, and growth.

Across sectors, startups are already moving toward AI-first, agent-driven systems that automate workflows, improve efficiency, and enable real-time decision-making. On April 24, 2026, a Snowflake x AWS Mixer brought together founders, operators, and investors in Bengaluru to discuss how AI innovation is reshaping the startup ecosystem. The evening featured live demos, panel discussions, audience Q&As, and networking over cocktails and dinner.

At the heart of the event was the panel discussion, From Data to Decisions: Building the AI-First Startup. Featuring Saurav Swaroop, Co-founder and CTO, Velocity; Pramod Rajagopal, Head of Solutions Engineering (Enterprise India/South Asia), Snowflake; Sachin Nandwana, Co-founder and Director, BigHaat India; Smita Satyavada, Cloud Sales Leader, AWS; Abhishek Mishra, Principal, Arkam VC; and moderated by Shivani Muthanna, Senior Director, Content Partnerships, YourStory, the conversation explored how startups are architecting agentic AI systems, balancing scale with cost, and using data as a long-term competitive edge.

What startups are actually building with AI

Across industries, startups are moving beyond experimentation and embedding AI directly into core operations.

At Velocity, Saurav Swaroop described an internal-first approach in which AI agents are used to reduce overheads and improve efficiency. The company has seen a 4x increase in productivity, while customer support systems now resolve a significant share of tickets without human intervention. AI also powers logistics workflows, from automated calls for non-delivery reports to order confirmations. Products such as Vani, an AI calling agent, extend these capabilities into the shipping stack to reduce revenue loss.

At BigHaat, Sachin Nandwana spoke about using AI to address fragmentation in agriculture. The company’s AI-powered ecosystem supports crop disease detection, predictive weather insights, and cropping pattern analysis. By combining geolocation, crop-stage data, and community inputs, the platform enables farmers to make better decisions at scale.

From an investor perspective, Abhishek Mishra pointed to the growing shift toward outcome-driven AI. As AI expands into sectors such as healthcare, legal, and education, he highlighted the importance of local context, intuitive interfaces, and proactive problem-solving.

Smita Satyavada added that startups are increasingly adopting a hybrid technology approach, using open-source tools for differentiation and proprietary systems for scale and reliability. With performance gaps between models narrowing, execution and speed are becoming more important than model choice alone.

The rise of decision-grade data

As AI systems become more sophisticated, the biggest challenge is no longer intelligence, but context.

Swaroop stressed the importance of digitization and structuring data in usable formats. The more connected and organized the data, the better the outputs generated by AI systems.

Mishra argued that data alone may not remain a long-term moat. What matters more now is how startups structure and use that data through strong frameworks that enable consistent decision-making.

Building on this, Rajagopal introduced the idea of “decision-grade” data: information that is not just clean, but reliable enough to drive business decisions. This requires continuous monitoring, architectures such as multi-agent systems, and semantic layers that provide context. He pointed to the growing role of synthetic data to simulate edge cases, while cautioning that overdependence on such datasets can sometimes distort outcomes.

Satyavada simplified data quality into four essentials: accuracy, timeliness, completeness, and governance. She emphasized the need for real-time checks, continuous audits, and clear traceability so that every AI-driven decision can be linked back to its underlying data source. Data quality, she noted, is no longer a one-time exercise but an ongoing operational discipline.

Agentic AI: Balancing autonomy with oversight

As startups increasingly adopt agentic AI systems, the conversation is shifting from capability to control.

Swaroop distinguished between internal and customer-facing use cases. Internally, Velocity allows higher levels of autonomy to maximize efficiency. Externally, however, workflows remain more structured, with clearly defined human handoffs to minimize risk.

Nandwana said the company follows a gradual rollout strategy. AI outputs are continuously monitored and only deployed once they meet predefined accuracy thresholds. Models are retrained regularly, and corrections are communicated quickly when required.

Mishra emphasized the importance of robust evaluation frameworks. Startups that invest early in testing and improving agent performance, he said, are better positioned to move from human-assisted systems to fully autonomous ones.

Satyavada recommended designing systems that anticipate failure rather than assume perfection. She advised separating hard rules, which cannot be violated, from flexible ones that allow adaptation. Multi-agent setups, where agents validate each other’s outputs, are also emerging as a practical way to improve reliability.

Rajagopal reinforced the need for governance from day one. Auditability, semantic context layers, and coordinated multi-agent systems are essential for production-ready AI environments.

Inside Snowflake’s agentic AI blueprint

The event also featured a live demo by Bharath Suresh, Senior Partner Solutions Engineer, Snowflake, titled The Blueprint: From Data to Action with Agentic Workflows.

Suresh outlined how Snowflake, in partnership with AWS, is helping startups move from static data systems to real-time, AI-driven decision-making. With more than a decade of collaboration, 50+ integrations, and a majority of customers operating on AWS, the partnership aims to simplify how businesses build, scale, and monetize AI systems.

He highlighted Snowflake’s evolution from a data warehouse into a full AI and data cloud platform that supports the complete lifecycle: data ingestion, engineering, analytics, machine learning, and GenAI.

A major shift, he explained, is the ability to bring models directly to the data, allowing startups to work with leading LLMs within the platform, without complex integrations. This enables conversational, real-time data exploration rather than only static dashboards.

Suresh also highlighted Snowflake’s fully managed, pay-as-you-go infrastructure model, which eliminates the need for server or cluster management. Features like Cortex AI and Cortex Code enable low-code, spec-driven development, enabling startups to build pipelines, applications, and agentic workflows through simple prompts and significantly reducing development timelines.

The demo also showcased Snowflake Intelligence, an interface that lets users query structured and unstructured data conversationally, and combine insights across systems, including AWS data sources, without manual effort.

With built-in governance, security, and a large marketplace ecosystem, Snowflake is positioning itself as a unified platform for startups looking to move quickly from idea to production while turning data into actionable, decision-grade intelligence at scale.

The evening concluded with a networking session over cocktails and dinner.