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[AgentSpot Showcase Series] Winny - GTM Intelligence Agent

Meet Winny, a GTM Intelligence agent built with AgentSpot and ThoughtSpot. See how teams can get faster answers to questions about conversion and pipeline velocity by simply asking questions in AgentSpot or Slack, with verified data pulled directly from ThoughtSpot. What is AgentSpot? AgentSpot is multiplayer AI for your business. Anyone can build, share, and collaborate with AI agents connected to your company’s data, context, and tools.

Is Your Data Estate Actually Ready for AI? The 6 Characteristics That Matter

Most organizations are moving fast on AI ambition. Fewer are moving fast on what makes that ambition possible. Before you can reimagine your business with AI at its heart, your data estate needs six things: to be well-defined, trusted, well-connected, contextualized, consumed in a multimodal way, and ready for both humans and machines at scale. Most organizations have two or three. The ones pulling ahead in AI have all six.

Centerprise AI: Add New Banking Systems Without Custom Integration Projects

Your next banking initiative shouldn't wait on another custom integration. Keep your core banking system and connect everything around it with Centerprise AI. Describe the pipeline you need, and it generates the connections, mappings, transformations, and data quality checks across APIs, databases, legacy systems, and more.

9 Low-Cost SaaS Metrics & KPI Dashboard Tools That Are Ridiculously Easy to Set Up

The best SaaS metrics dashboard software doesn’t have to cost enterprise money. Every tool on this list is priced for growing companies, sets up without a demo call, and pulls your key metrics out of the apps where they’re trapped. If you’re a SaaS company specifically, we’ve built a dedicated home for this problem: see how Databox works as the best SaaS metrics dashboard software for finance, product, and go-to-market data.

From Chatbot to Compound AI System: Infrastructure Patterns for Multi-Model, Tool-Using Applications

Two years ago, GenAI in production usually meant a single LLM serving a single endpoint. In 2026, it usually means much more. The applications shipping in front of users today are compound AI systems: orchestrated pipelines of retrievers, embedders, dialogue models, classifiers, code interpreters, SQL executors, and tools, with a single user request fanning out to several model calls across the stack.