Unisight

Demo

About

### Core Concept **Unisight** is an AI-powered platform that enables anyone—regardless of technical background—to explore, visualize, and analyze on-chain data through natural language. By removing the need to write SQL queries or code, Unisight makes blockchain data truly accessible. The core idea is to turn data interaction into a conversational experience, helping users unlock insights effortlessly. ### Key Components 1. **AI Interaction Layer (Powered by GPT-4o):** Unisight leverages OpenAI’s GPT-4o model to interpret natural language requests and translate them into database queries, visual instructions, or analytical summaries. 2. **MCP Server-Based Data Access:** The platform wraps blockchain databases into a modular MCP server format, allowing the AI to query and interact with data efficiently and securely. 3. **Visualization Engine (Plotly-Powered):** The system uses Plotly to generate rich, interactive JavaScript visualizations with features like zooming, tooltips, and drill-down capabilities, all rendered directly in the frontend for seamless user interaction. 4. **Insight Generation Module:** Beyond just charts, the AI interprets patterns, explains trends, and suggests deeper insights to guide users through complex data narratives. ### Key Features - **No Code, Just Language:** Users can type questions like “What’s the trend of NFT sales on Ethereum this month?” and immediately receive charts and AI-generated insights. - **Plug-and-Play Data Integration:** Any on-chain data source can be connected to Unisight via its MCP server structure, enabling rapid expansion across ecosystems. - **End-to-End AI Co-Pilot Experience:** From querying to charting to insight generation, the AI stays with the user every step of the way, allowing follow-up questions and deeper dives. - **Designed for Non-Technical Users:** Perfect for creators, analysts, investors, or anyone curious about blockchain data but unfamiliar with SQL or programming. - **Interactive, Conversational Analytics:** The system adapts to follow-up prompts, supports drill-downs, comparisons, and lets users refine their exploration naturally.

How it's made

### 🧠 AI Architecture Unisight is built around a **modular multi-agent architecture** designed to translate natural language prompts into dynamic, interactive visual analytics. 1. **Planner Agent** The user prompt is first passed to a *Planner Agent*, which breaks down complex questions into sub-tasks such as data querying, visualization, or follow-up analysis. This modular planning enables Unisight to handle abstract and multi-step queries. 2. **Router Agent** During user interaction—especially when users engage with visualizations or images—the *Router Agent* determines the intent of the prompt. It decides whether the user wants to regenerate the chart, request further analysis, or ask a follow-up question. 3. **Visualization Agent** Once data is retrieved, the *Visualization Agent* receives flattened JSON-formatted data and generates the appropriate Plotly configuration. The agent is optimized to understand chart types, axes, dimensions, and interactivity features from both data structure and natural language cues. --- ### 🔌 Data Access via MCP Server Unisight introduces a flexible, AI-friendly abstraction for blockchain data access using an **MCP (Model Control Protocol) Server**: - Any on-chain database can be connected to Unisight by exposing its API in a standard format (Request Headers, Request Body, Response Body returning JSON). - The AI model interacts with these APIs by sending structured calls to the MCP Server, which bridges the language model and the raw data source. - This allows **plug-and-play integration** of any on-chain data source without the AI needing hardcoded logic. > 🧩 **Current Integrated Data Sources** > Unisight currently connects to APIs provided by **Nodit**, **1inch**, and **Zircuit**, allowing rich access to real-time and historical on-chain activity including DeFi trades, protocol metrics, and ecosystem analytics. --- ### 📊 Visualization Layer (Plotly-Driven) - Unisight uses **Plotly** to generate interactive, JavaScript-based charts. The Visualization Agent outputs Plotly chart definitions, which are converted to JS and rendered on the frontend. - Features supported: zoom, pan, tooltip, drill-downs, and dynamic updates via follow-up prompts. - Users can re-engage with a chart (e.g., “add a moving average” or “compare this with last month”) and trigger re-plotting, all within the same natural language loop. --- ### 🔔 Webhook Automation via Natural Language (Powered by Nodit) Unisight extends its capabilities beyond data analysis by enabling users to create **on-chain data webhooks via natural language commands**, using Nodit's API infrastructure. - Users can simply say things like: *“Alert me when daily active users on Zircuit exceed 10,000”* *“Notify me when there’s a large swap on 1inch over $100k”* - Behind the scenes, Unisight parses the intent and dynamically constructs the correct **webhook configuration**, including filters, conditions, and destination URLs. - These webhooks are registered through Nodit’s webhook API, and users are provided with a corresponding **visual webhook dashboard** where they can manage, edit, or remove their alerts. This feature turns passive analytics into **active monitoring and alerting**, making Unisight not just a dashboard, but a real-time data assistant. --- ### 🔁 System Architecture Unisight’s system is composed of three main layers: 1. **AI Layer** - Powered by OpenAI GPT-4o - Manages prompt understanding, planning, database interaction, visualization generation, and conversational flow - Tracks session history for contextual multi-turn conversations 2. **Backend Layer** - Hosts the MCP Server, which interfaces with blockchain data APIs like Nodit, 1inch, and Zircuit - Handles data flattening and preprocessing for visualization agents - Manages session state, logging, and inter-agent communication 3. **Frontend Layer** - Renders AI-generated Plotly charts and facilitates user input - Supports interaction through text and visuals (e.g., clicking a chart to trigger deeper analysis) - Displays AI commentary and updates based on user refinements