Databox is an AI-powered business intelligence and analytics platform for teams that need clear, trusted answers fast. It offers the best of BI, without the complicated setup, steep price, or long learning curve. It provides a blend of powerful, but easy-to-use, features, from preparing datasets and creating the custom metrics your company needs to track, to building beautiful dashboards, customizing your reporting, and receiving AI-powered insights.
Products used by Databox
Explore the tech stack and tools that power Databox. See what products Databox uses for development, design, marketing, analytics, and more.
Design & Creative 1
Design & Creative 1
Amazon BedrockEasiest way to build and scale generative AI applications
5.0 (4 reviews)
For Genie's RAG layer, we needed managed infrastructure that wouldn't become its own engineering project to maintain. Bedrock let us connect our knowledge base, manage embeddings, and keep retrieval fast - without running our own vector infrastructure. The alternatives either required too much ops overhead or didn't integrate cleanly with the rest of our stack. Bedrock just worked, and that let us stay focused on the product.
Engineering & Development 3
Engineering & Development 3
Claude CodeAnthropic’s deep-context AI coder
5.0 (538 reviews)
Claude was our AI pair programmer throughout the build. We used it to generate API connection configurations, work through edge cases in our dataset logic, and move faster across the entire development cycle. It also powers the AI-assisted setup experience we built for users - paste your API docs into Claude, get a ready-to-use configuration back.
No-code Platforms 1
No-code Platforms 1
LLMs 3
LLMs 3
LangchainLangChain’s suite of products supports AI development
4.9 (108 reviews)
We evaluated several orchestration frameworks before choosing LangGraph for Genie's agent architecture. The alternatives either abstracted too much away - making it hard to control exactly when and how tools get called - or couldn't handle the stateful, multi-step flows we needed. LangGraph gave us the right balance: explicit control over agent logic without building everything from scratch. When your AI is touching live business data, you can't afford unpredictable behavior.
General 2
General 2
LangSmithThe platform for agent engineering
Once Genie was running in production, we needed visibility into what the agent was actually doing - not just whether it returned an answer, but whether it made the right tool calls in the right order. LangSmith was the clearest choice for tracing agentic workflows end-to-end. Other options gave us logs; LangSmith gave us understanding. That difference matters when you're debugging why an AI analyst gave a wrong answer to a business question.
