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 (688 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
n8nWorkflow automation for technical people
4.8 (74 reviews)
The workflow half of the marketplace runs on n8n. The .json workflows handle scheduled automation - weekly SEO reports to Slack, daily paid ads briefs, traffic spike alerts - all powered by live Databox data. n8n makes it possible to ship "set it and forget it" analytics without any coding.
LLMs 3
LLMs 3
LangchainLangChain’s suite of products supports AI development
4.9 (115 reviews)
We evaluated several orchestration frameworks before choosing LangGraph for Artifacts. Generating a document isn't one call and done, it's a multi-step flow: pull the data, structure the layout, apply styling, and hold state across follow-ups like "turn this into slides." The alternatives either abstracted too much away or couldn't handle that stateful, multi-step process cleanly. LangGraph gave us explicit control over each step, so a report doesn't drift into something the user didn't ask for halfway through generation.
OpenAIAPIs and tools for building AI products
5.0 (831 reviews)
We use OpenAI alongside Claude for the structured side of every run: pulling the right metric data, parsing what the routine was asked to do, and shaping the result into something clean and consistent, run after run. When a routine fires at 7am with nobody watching, that formatting has to be dependable every single time, not just most of the time.
Claude by AnthropicA family of foundational AI models
5.0 (990 reviews)
Routines run without anyone watching. That means the reasoning has to hold up on its own, no follow-up question to catch a mistake, no chance to rephrase and try again. Claude does the actual analysis behind every run: reading the data, working out what changed and why, and writing a result that has to be right the first time, because for most runs, no one checks it before it lands in someone's inbox.
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.
