Nitin Singhal is the Vice President of Engineering at SnapLogic, where he focuses on responsible integration of applications and systems. SnapLogic is an AI-powered integration platform that streamlines data and application workflows with over 1,000 pre-built connectors
Singhal has over 25 years of experience in the industry and has previously held various engineering and product leadership positions at prominent organizations, including Twitter and Visa.
Obsolete systems, particularly legacy infrastructures, lead to crippling inefficiencies and prevent organizations from harnessing AI for high-value tasks. These outdated technologies struggle with data accessibility and integration, creating costly operational bottlenecks that hinder automation and innovation.
SnapLogic's platform empowers businesses to unify and automate workflows across data and applications, bridging legacy systems with modern, AI-ready infrastructure. By seamlessly connecting fragmented data sources and simplifying integration across cloud and on-premises environments, SnapLogic accelerates the transition to a unified system where AI can deliver immediate value.
SnapLogic’s Generative Integration is a cutting-edge feature of SnapLogic's platform that utilizes generative AI and large language models (LLMs) to streamline and automate the creation of integration pipelines and workflows.
SnapLogic offers immense flexibility by allowing customers to utilize any public LLM models tailored to their specific needs.
Industries that rely on data-driven decision-making and repetitive tasks stand to benefit the most. For instance, in the financial services sector, AI can automate tasks like customer support, fraud detection, and loan approvals, streamlining operations and enhancing the customer experience.
Businesses can evaluate their AI readiness by assessing whether their current systems can integrate with modern AI tools and scale to meet increasing data demands.
With SnapLogic AgentCreator, organizations can deploy AI agents that automate tasks, make real-time decisions, and adapt within existing workflows.
Cutting-edge observability, security, and privacy protection techniques following composable architecture are critical for seamless and responsible AI readiness.