I’m Daniel. I’ve spent the last few years building internal tools and automations for companies like FINN and Personio.
I built PUNKU.AI to solve a frustration I had with the current state of automation.
Right now, you generally have two choices:
Manual: Dragging and dropping nodes in tools like Make/Zapier (reliable, but tedious for complex logic).
"Magic" AI Agents: Giving a prompt to an agent and hoping it works (fast, but impossible to debug or trust with sensitive data).
PUNKU.AI is an attempt to bridge the gap. It uses an LLM to "architect" the workflow, but it outputs a fully editable visual node graph (JSON-based) that you can verify before running.
How it works:
1. The "Interview": Instead of taking a zero-shot prompt, the system acts proactively. It parses your request and asks clarifying questions to resolve ambiguity (e.g., "If the API returns a 404, should I retry or alert you via Slack?").
2. Graph Generation: It generates the logic, loops, and branching, visualizing it as a node graph. You can manually tweak connections if the LLM hallucinated a step, or ask it to regenerate specific parts.
3. Integrations: We are connecting to ~3,000 API endpoints. The agent handles the authentication handshake and schema mapping.
The Tech:
We are focusing heavily on the "translation layer" between natural language and structured automation flows. The goal is to have the speed of a chat interface but the determinism of a state machine.
I’d love for you to try breaking it. Ask it to build something complex (loops, conditional logic) and let me know if the generated graph makes sense.
Dquiroga•2mo ago
I’m Daniel. I’ve spent the last few years building internal tools and automations for companies like FINN and Personio. I built PUNKU.AI to solve a frustration I had with the current state of automation.
Right now, you generally have two choices: Manual: Dragging and dropping nodes in tools like Make/Zapier (reliable, but tedious for complex logic). "Magic" AI Agents: Giving a prompt to an agent and hoping it works (fast, but impossible to debug or trust with sensitive data).
PUNKU.AI is an attempt to bridge the gap. It uses an LLM to "architect" the workflow, but it outputs a fully editable visual node graph (JSON-based) that you can verify before running.
How it works: 1. The "Interview": Instead of taking a zero-shot prompt, the system acts proactively. It parses your request and asks clarifying questions to resolve ambiguity (e.g., "If the API returns a 404, should I retry or alert you via Slack?").
The Tech: We are focusing heavily on the "translation layer" between natural language and structured automation flows. The goal is to have the speed of a chat interface but the determinism of a state machine.I’d love for you to try breaking it. Ask it to build something complex (loops, conditional logic) and let me know if the generated graph makes sense.
Link: https://www.punku.ai
Thanks!