Visual workflows for LLM-powered analysis

Design LLM workflows that turn information into decisions

Build visual braids that ingest documents, query knowledge, call tools and produce structured outputs without wiring together a custom stack for every use case.

Visual braids Knowledge-aware workflows LLM analysis Webhook-ready outputs

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Example workflow

Document intake to structured brief

Webhook ready

Why teams use DataBraid

A clearer way to build LLM workflows for analysis and operations

DataBraid is built for teams that need more than prompt experiments. It gives you a visual way to combine sources, knowledge, models and outputs into workflows that can be understood, iterated and eventually exposed as reusable services.

Too many moving parts

Teams end up stitching together prompts, scripts, retrieval, APIs and outputs across multiple tools with little visibility into how the workflow actually works.

One-off prompt engineering

Useful analysis often depends on context, documents, external tools and repeatable processing steps. A chat box alone is rarely enough.

Hard-to-maintain automations

When data pipelines live in code only a few people understand, iteration slows down and operational handoffs become fragile.

How it works

Build the workflow once, then run it with context

DataBraid is designed first as a visual studio for braid design. You can test manually while iterating, but the same workflow can later be called through webhooks or other integrations when it is ready for real use.

Compose the braid

Design a visual workflow that connects sources, transforms data, invokes models and defines exactly how results are produced.

Add context and analysis

Bring in knowledge bases, retrieval and third-party tools so each run works with evidence, not just a prompt.

Deliver it operationally

Test manually while designing, then expose the braid through webhooks or integrations when the workflow is ready to run.

Use cases

Focused on useful outputs, not generic AI demos

The strongest use of the platform is when LLMs operate inside a workflow: with sources, constraints, retrieval, external tools and a defined output shape.

Document analysis

Turn files and notes into structured briefs, findings, risks and next actions.

Research synthesis

Combine web search, external tools and LLM reasoning into repeatable research workflows.

Knowledge-grounded Q&A

Use retrieval over curated knowledge so answers can work with real context instead of generic model output.

Automation behind webhooks

Design the flow visually, then trigger it from other systems once the logic is stable.

Start with the right workflow

Design braids for analysis first. Operationalize them when they are ready.

Join the beta to explore visual workflows for LLM-powered analysis, knowledge-grounded outputs and webhook-ready automation.

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