Bolt
An AI-native notebook inside the PredictHQ WebApp. Bolt answers from live PredictHQ API data—a working example of a grounded agent.
Bolt combines a conversational AI interface with a persistent notebook of interactive cards. Ask questions in natural language, and Bolt retrieves real data from PredictHQ's APIs, guides you toward the right products for your situation, and surfaces results as cards you can explore, share, and take directly into your integration.
Bolt is also a working example of grounding: it answers from verified, live PredictHQ data retrieved at the moment you ask—not from what a model guesses. The same capability is available to your own AI stack through the PredictHQ MCP server.
What you can do
Explore PredictHQ data through conversation. Ask about events, demand signals, locations, and forecast inputs in plain language. Bolt queries PredictHQ's APIs on your behalf and returns real results - not synthetic examples.
Get guided to the right solution. Bolt understands PredictHQ's products and how they fit together. Rather than returning a generic answer, it steers you toward the right approach for your use case - running a Beam Analysis before querying features, using Predicted Impact Area rather than a fixed radius, and following the integration patterns that produce the best outcomes.
Build with the results. Every card in the notebook includes a production-ready API code snippet alongside the visualization and raw data. When you find something useful, the code to reproduce it is already there.
Work iteratively. Cards persist across the session and accumulate in the notebook as the conversation progresses. Follow up, refine, and drill down - the notebook becomes an artifact you can revisit and share with your team.
How it works
Bolt's interface has two panels that work together.
The chat panel is where you type questions and see responses. Bolt can execute multi-step workflows - creating a Saved Location, running a Beam Analysis, and returning demand-calibrated features - from a single conversational exchange.
The notebook panel is where results appear as cards. Each card has three tabs:
Preview - an interactive chart or map of the results
Code - a production-ready API snippet you can take directly into your integration
Data - the raw API response
Bolt manages cards as the conversation evolves - it collapses cards that are no longer relevant, while cards you pin stay in view. The notebook is the persistent artifact; the chat is how you drive it.
PredictHQ best practices built in
PredictHQ configures Bolt with its integration best practices. It follows the recommended workflow automatically:
Creates Saved Locations before running Beam Analyses
Uses Predicted Impact Area rather than fixed radii
Runs Beam to identify which events drive demand at each location before retrieving features
Uses the Features API for demand signals and the Events API for drill-down and explainability
Applies industry context to calibrate results
This means Bolt produces better results out of the box than querying the APIs directly without guidance - and helps you understand why each step matters as you work through it.
Getting started
Bolt is available inside the PredictHQ WebApp. In the navigation, select Bolt to open the interface.
Start by describing your use case or location - for example:
"What events are impacting demand at my hotel in Chicago next month?"
"Set up a Beam Analysis for our restaurant in Sydney"
"Show me demand signals for a retail location in London"
Bolt guides you from there.
Next steps
Continue with these pages:
MCP server - connect Bolt's capabilities to your own AI assistant or coding environment
Agent skills - install PredictHQ best practices into your AI coding agent
Integration guides - move from exploration to a production integration
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