LLM Flow DesignerBuild AI agent networks
you can actually control
Specify exact paths, inject the right context at each step, and track every execution. No agent-framework code. No flying blind.
Design in SaaS · run with your keys in your infrastructure
Deterministic agents are hard.
Frameworks make them harder.
Reliable graphs need precise context, tool parameters, and repeatable paths. Code-first frameworks bury that control under SDK overhead.
What teams fight today
- Opaque graphs. No clear view of which path a prompt took.
- Context overload. Dumping more data into prompts reduces reliability.
- Brittle orchestration. LangChain, CrewAI, AutoGen, custom SDKs. More code, less visibility.
- Change latency. Prompt tweaks wait on engineering tickets and deploys.
What the builder gives you
- Visual paths. See and specify the exact route each request follows.
- Field-level context. Pass only the data each node needs.
- Quorum and loops. Repeat until consensus. Verify before continuing.
- Live control. Inspect timing, cost, and path. Adjust without redeploying code.
Prompt in. Network in between.
Output you can trust.
Start with an input node, end with an output node. Everything between is your agent network: data, tools, branches, loops, and LLM steps.
Compose the graph
Wire LLM calls, API connectors, Drive docs, conditionals, and tools. Map fields between nodes with precision.
Run and observe
Watch each step, path, and token cost in real time. Catch failures where they happen, not in log archaeology.
Ship on your terms
Keep designing in SaaS. Export production runs to your infrastructure with your keys so compliance stays intact.
Patterns you need
without writing the graph in code
What we built for
documents, AP, and dynamic context
Deep pages for the pipelines that already ship: PDF rasterize and chunking, document extraction, Drive/API/MCP knowledge loading, and structured multi-step agents.
Rasterize, select, chunk
Image-page PDFs, page ranges, parallel chunk merge, and flow-file attachments for vision-ready extraction.
Read the use case DocumentsField extraction
Header fields, optional playbook loading, detail rows, filters, and quorum on values that must stay stable.
Read the use case KnowledgeDynamic document load
Resolve a name from the prompt, fetch Drive/API/MCP content, inject only that material into the next step.
Read the use case ProductPDF and file pipeline
How S3, FlowFiles, tabular row chunking, and parse fallbacks work under the canvas.
Product deep diveConnectors and pipelines
on the same canvas
Drive, API, URL, MCP
Enrich prompts from Google Drive, REST, URLs, and MCP servers with encrypted credentials and expression search.
PDF rasterize and chunk
Turn weak text-layer PDFs into image pages, select pages, chunk long files, and attach FlowFiles downstream.
Quorum, loops, filters
Consensus on fields, per-item nested graphs, and pre-prompt filtering so models see candidates not warehouses.
Your infrastructure. Your keys.
Design flows in our SaaS. Run them where your data already lives. Only execution logs and performance metrics return to the platform.
Design phase
Build, test, and iterate visually. Share flows across your tenant without touching production secrets.
Production phase
Export and execute in your environment. Keep API keys, documents, and PII under your control.
Business owns the agent.
Engineering owns the platform.
Stop paying senior engineers to edit prompts. Product and operations can ship flow changes the moment the business needs them.
Without the builder
- Every prompt tweak is an engineering ticket.
- Ideas stall in sprints while competitors iterate.
- Subtle wording changes quietly break financial agents.
With the builder
- Analysts adjust paths and context in minutes.
- Developers focus on connectors, security, and scale.
- Split meanings into separate visual paths and verify with quorum.
Questions teams ask first
Do I need developers to change a flow?
No. Product owners and analysts can edit prompts, branches, and context in the visual builder and publish immediately. Engineers stay free for infrastructure and integrations.
Can you handle scanned or broken PDFs?
Yes. The PDF Rasterize node turns pages into embedded images. Page selection and chunking split long files. Downstream LLM nodes attach the FlowFile. Tabular attachments can chunk by rows instead.
Where does production data live?
Design and iterate in our SaaS. Export and run flows in your infrastructure with your own API keys. Only logs and metrics come back to the platform.
How is this different from LangChain or CrewAI?
Those tools are code-first orchestration frameworks. LLM Flow Designer is a visual execution graph: precise paths, field-level context, quorum, PDF pipelines, and live tracking. See the compare page for the trade-offs.
What does early access pricing mean?
Soft launch is pay-what-you-think, minimum $5/month. Highest supporters get access first in weekly waves. There is a 7-day money-back guarantee after activation.
Reserve early access
Soft launch with pay-what-you-think pricing. Highest supporters get in first. Design in SaaS. Run in your infrastructure.