How reelay Turns Goals Into AI-Powered Workflows

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How We Built reelay: Turning Goals Into Workflows

Less back-and-forth. More moving forward. What if you could explain what you want to achieve, and an app could create a plan using the tools and services needed to make it happen? That was the idea behind reelay, the project our team created at the hackathon. reelay is built as a general-purpose tool for managing plans. A person shares a goal, and the system creates a series of connected steps that can involve different tools and businesses. For our demo, we picked one scenario: organizing an accessible day out, including a place to eat, an activity and a way to move between them. The approach can also be applied to many other kinds of tasks. Here’s how we made it. 01 / We made the graph the foundation A plan becomes a graph of tasks. Each node represents a step, and the links show how those steps are connected. This helps the user and the system see what needs to be done and in what order. The same structure can represent different types of work, whether the request involves finding information, bringing services together or completing steps that depend on each other. 02 / We used Gemini to create plans A person describes a goal in their own words. Gemini turns the request into a graph, which the system checks and saves. This gives users a plan they can inspect without building every step themselves. 03 / We made businesses easier to find We built a search tool using Qdrant and embeddings. It finds businesses based on a natural-language request and can filter results by location, type and cost. We made it available through MCP, so agents and the graph system can use it as part of a plan. 04 / We created services for the demo To show how different services can work together, we made MCP providers for dining, activities and transport. They can search, create an intent and fulfill it. Our Timișoara example brings them together in an accessible day out. We also added a transport disruption to explore how a plan can change when things go wrong. The providers use fictional demo data. 05 / We added voice with ElevenLabs We wanted users to be able to speak their goals, so we added voice-to-text, editable transcripts and audio playback for results. Users can check what the system heard before submitting their request. 06 / We built the workspace and login system We used Next.js for the frontend, Django for the backend and PostgreSQL for storage. Auth0 handles login, giving users a place to create, inspect and return to their graphs. 07 / We built a Solana escrow prototype We wrote a program in Rust with Anchor to explore how a person could fund a plan and have money released step by step. The program supports deposits, individual releases, top-ups and refunds, with a record for each payment. We deployed it to Solana devnet and ran a test that locked 52 tokens, sent payments to two recipients and refunded the rest. Connecting the escrow prototype to the app is the next step. The biggest challenge: making all the APIs work together Gemini, ElevenLabs, Auth0, MCP providers, business search and Solana each had their own formats, assumptions and ways things could go wrong. Getting each part to work on its own was only part of the task. The harder part was making the output of one part useful to the next while keeping the process clear for the user. We worked through it by giving each part a clear role: understand the request, build the plan, find the right services, carry out steps and manage payments. The main thing we learned: a good integration is more than a set of APIs. It makes them feel like one coherent product. The trip example is one use case. The bigger idea behind reelay is a system that can turn a wide range of goals into steps and bring the right services together to help make them happen. Explore reelay Check out the code: reelay on GitHub

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