Visual braid design
Model the workflow explicitly: inputs, sources, transformations, LLM steps, outputs and operational entrypoints all live in the same canvas.
Features
DataBraid is not positioned as a generic AI playground. It is strongest when you need to connect sources, retrieval, models, tools and outputs into a workflow that can be read, improved and eventually run operationally.
Model the workflow explicitly: inputs, sources, transformations, LLM steps, outputs and operational entrypoints all live in the same canvas.
Pull in files, documents and external data sources so the workflow starts from the information your team actually uses.
Use models to summarize, compare, classify, extract and generate structured outputs instead of relying on a single prompt box.
Ground runs with knowledge bases and retrieval so analysis can work with curated context, not only the model's prior knowledge.
Extend braids with external tools and MCP servers when the workflow needs capabilities beyond the model itself.
Test flows manually while designing them, then expose them operationally through outputs and webhook-driven execution.
How the pieces fit
The product becomes easier to understand when described as a system rather than as a list of isolated features. These are the four layers that matter most in practice.
Text, files, documents and connectors enter the braid as explicit inputs instead of hidden pre-processing.
Nodes transform content, invoke models, structure results and prepare the output the workflow is supposed to produce.
Retrieval and MCP-style tools bring evidence and specialized capabilities into the run when the task requires them.
The same braid used for design can later be called through webhooks or attached to other systems once validated.
Build with the right context
If your workflows need sources, knowledge, models and automation to work together, the beta is the right place to evaluate the product.