Build Adaptive AI Loops
Create recursive loops that evolve through real-world data and human interaction. They adapt and optimize with every iteration.
The No-Code Builder For Self Learning AI Prompt Loops.
Build AI prompt chains that access real-world data and human interactions, so they can adapt, optimize, and learn autonomously by themselves.
Static prompt chains operate in a vacuum.
Standard LLM workflows are brittle. They can't verify their own assumptions, they hallucinate facts they can't check, and they are disconnected from the real-time state of the world. They follow instructions, but they cannot truly learn from the outcome of their actions.
Connect your prompts to reality.
loopthink.ai is the engine that bridges this gap. We provide the infrastructure to build intelligent loops where each step can be validated against external sources. This feedback mechanism transforms a simple chain into a self-learning system that improves with every execution, grounded in empirical data.
Design. Validate. Evolve.
1. Design Your Loop
Visually chain prompts, logic, and actions. Define the steps your AI should take to solve a complex problem—no code required.
2. Inject Real-World Validation
This is the core of loopthink. Add validation nodes to your workflow: query a web service, check an API endpoint, poll a database, or request human feedback.
3. Execute & Learn Autonomously
Deploy your workflow. Our engine runs the loop, collects the results from your validation sources, and uses this data to intelligently guide, refine, and optimize the next iteration.
From simple chains to autonomously learning agents.
Domain Availability
Loop: Generate brand names → Validation: Check domain availability via an API → Learning: Refine the next generation of names based on which patterns are still available.
SEO & Keyword Traffic
Loop: Generate a blog post for a topic → Validation: Query an SEO tool's API for keyword traffic and competitiveness → Learning: Rewrite sections to better target high-potential keywords.
Live Data Integration
Loop: Formulate a research hypothesis → Validation: Make an HTTP request to a public data source (e.g., stock market, weather API) → Learning: Adjust the hypothesis based on the retrieved real-time data.
Human-in-the-Loop
Loop: Create three design variations for a UI element → Validation: Present them to a user or team for a simple vote → Learning: Use the collective feedback to inform the next design iteration.
Stop prompting. Start building AI loops.
Ready to build AI workflows that actually learn? Get access to the loopthink.ai engine and connect your models to the real world.