You keep re-explaining yourself
Every new chat, you paste the same context, the same style guide, the same examples. AI has amnesia. You're the IT support.
AI Workflow Builder
Turn repeat tasks into AI workflows that run themselves.
Describe what you need. FlowPilot builds the multi-step AI workflow, connects it to your tools, and runs it whenever you want. No prompts to rewrite. No code to debug.
Concept project · No signup required
Flow Preview
Read Gmail
Every weekday at 9am
Summarize with AI
Extract top 3 themes
Post to Slack
#inbox-summary
Running
Built for
Founders running multi-tool businesses without an engineering team.
You know what should be automated. You just don't have a developer to build it.
- Lead enrichment New signup enrich profile score notify sales
- Customer follow-up Ticket closed schedule check-in log to CRM
- Content distribution Draft published reformat cross-post track engagement
AI is smart. Using it is still manual.
The bottleneck isn't intelligence. It's orchestration.
Outputs disappear after one use
A great summary today. A good draft yesterday. Each one trapped in a conversation—never reused, never improved, never shared with your team.
AI can't touch your actual work
Your emails, docs, and Slack all live somewhere else. You're still the middleware—copying between the chat tab and everywhere real work happens.
A workflow layer, not another chatbot.
AI got smart. Workflows didn't catch up. FlowPilot fills the gap between one-shot prompts and rigid automation.
ChatGPT-style
Zapier-style
FlowPilot
Multi-step orchestration
✕
One-shot only
◐
Rigid rules
✓
AI-native steps
AI-native reasoning
✓
◐
Bolted on
✓
Built for non-coders
✓
◐
Steep curve
✓
Reusable across runs
✕
Re-prompt each time
✓
✓
AI-native, multi-step, no-code, reusable. All four at once — that's the gap.
Three things FlowPilot does that ChatGPT can't.
01 · Visual workflow canvas
Build flows by dragging, not coding.
Drop blocks on a canvas. Connect them with a line. Run the whole thing with one click. Each block is an AI step, a data source, or an action—arrange them however your work actually happens.
- 20+ block types: LLM call, web fetch, Slack post, Notion write, conditional branch
- Branch, loop, and merge—the flow handles the logic
- Preview every step's output as you build
02 · Plain English
Describe steps in plain English.
Tell FlowPilot what each step should do. It handles the prompts, the inputs, and the outputs. If it's wrong, fix it with a sentence—not a parameter panel.
- "Summarize this email in three bullets" → configured
- Edit any step by chatting with it
- Version history, so no good iteration is ever lost
03 · Connected tools
Reads your data. Writes to your stack.
FlowPilot plugs into the tools you already use. Pull context from Gmail, Notion, or any API. Push results to Slack, Linear, a spreadsheet, or an email draft. No more AI-in-a-tab.
- 30+ native integrations (Slack, Notion, Gmail, Linear, Sheets, and more)
- Trigger on schedule, event, or manual run
- Outputs go where your work lives
From idea to running workflow, in four steps.
Click any tab or watch it auto-advance. Hover to pause.
Describe what you need
Tell FlowPilot what you want to automate, in the same words you'd use to explain it to a coworker. No prompt engineering. No syntax to learn.
- Natural language, no special formatting
- FlowPilot asks follow-up questions if needed
- Your prompt becomes a reusable workflow spec
Design the flow
FlowPilot drafts a workflow for you. Visual. Editable. Transparent. See every step. Tweak the prompt. Swap a tool. Add a branch.
- Drag, drop, and connect steps visually
- Preview every step's output as you build
- Add conditions and branches without code
Connect your tools
Plug in the apps the flow needs. Authorize once, reuse forever. FlowPilot handles the API plumbing so you never touch a docs page.
- 30+ native integrations, one-click auth
- Shared across your workflows — connect once
- Or bring any HTTP endpoint via API block
Run it on your terms
Trigger manually. Put it on a schedule. Wire it to an event. Your workflow runs, reports back, and gets smarter every time you use it.
- Schedule by day, time, or interval
- Trigger from webhooks, Slack, or email
- Run logs for every execution, always searchable
Built for production from day one.
Every design choice — model routing, cost ceilings, async execution, graceful failure — exists because production AI doesn't tolerate hand-waving.
Right model, right step.
Claude Sonnet for reasoning. Haiku for classification. GPT for structured output. The router picks per step — not per workflow.
800ms
avg latency / step
Predictable, capped.
Hard caps per workspace. Cost preview before every run. No surprise bills when a workflow loops harder than expected.
$0.04
avg per run
Async by default.
Workflows run in the background. Get notified when done. The UI never blocks on AI — because users shouldn't wait on a model.
<2s
p95 to start
Graceful degradation.
Step fails → retry with fallback model. Still fails → human handoff with full execution log. No silent breakage.
99.2%
step success rate
We've heard them. Here's where we land.
Why won't OpenAI, Anthropic, or Google just build this?
Plausibly they will. Custom GPTs and Claude Skills already point at the workflow direction.
But foundation labs optimize for model capability, not business workflow integration. Building a real workflow product means owning OAuth flows for Slack, Notion, Salesforce, HubSpot, retry logic, audit logs, multi-tenant cost ceilings, role-based permissions — surface area that's orthogonal to model research.
The pattern repeats: AWS could have built Stripe. Google could have built Notion. They didn't, because verticalized integration is a different game than infrastructure.
Isn't this just Zapier with an LLM bolted on?
Zapier connects apps via deterministic if-this-then-that triggers. FlowPilot orchestrates AI reasoning across steps — meaning each step can interpret unstructured input, decide what to do, and adjust the flow's trajectory.
Concretely: a Zapier zap can't "summarize this email and decide if it's a sales lead, then route to the right pipeline based on the topic." Each italicized verb requires reasoning, not branching logic.
Zapier is a graph of triggers. FlowPilot is a graph of decisions.
What happens when a step in the workflow fails?
Three layers of fallback.
First, automatic retry with a fallback model — if Claude Sonnet times out, the step retries with GPT-4o or a smaller model.
Second, if the step still fails, the workflow pauses and surfaces a structured error with full execution log: what step, what input, what the model returned. Not a stack trace.
Third, every workflow has a human-in-the-loop checkpoint option for high-stakes steps — set once, applies forever.
Why wouldn't I just call the APIs myself?
If you have an engineering team, you should. FlowPilot isn't trying to compete with hand-rolled pipelines.
But for the founder running a multi-tool business without a developer — the time to set up an API key, write the script, deploy it somewhere with a cron, debug the prompts, monitor failures, and update it when an API contract changes — that's not "free." That's roughly 8-15 hours of tooling per workflow, then ongoing maintenance.
The right question isn't "could I do this myself" — it's "is the time worth more than $29 a month."
Every repeat task deserves a flow.
No signup, no setup. Just see the orchestration in action.
Try the interactive demo