Instadash
Giving AI agents a persistent data surface people can inspect and reuse.
A hosted surface that lets agents publish structured output into persistent, queryable grids for human review and later agent use.
View live product- Focus
- Agent data workflows
- Surfaces
- MCP + CLI + Web
- Status
- Current product
A chat window is not a data surface.
Agents produce structured research, logs and scraped output, but large tables are fragile inside conversation context. People still need a stable place to inspect, filter and share that data.
Instadash creates one persistent surface that can be written by agents, reviewed by people and queried again through web, API or MCP workflows.
One endpoint. Four connected moves.
- 01
Publish output
Send structured agent output through MCP, CLI or API.
- 02
Infer structure
Turn incoming data into typed, queryable grid columns.
- 03
Review the grid
Let people inspect, search and filter the persistent result.
- 04
Reuse the data
Query the same surface through API, embeds or agent tools.
One data surface. Two audiences.
- Strategy
- Frame the product around the hand-off between agent output and human review.
- Experience
- Design a clear grid, workspace and integration journey for technical users.
- Intelligence
- Infer usable structure while keeping the resulting data inspectable and editable.
- Engineering
- Join hosted MCP, ingestion, versioning, query and embed surfaces into one system.
Scope represented:Product strategyDeveloper experienceAI workflow designProduct engineeringCloud delivery
Designing the hand-off between agents and people.
Instadash shows how infrastructure can become a product experience: one operating surface that makes agent output persistent, reviewable and reusable.
This page documents the product and work represented. Commercial claims, customer outcomes and performance metrics are intentionally excluded until verified.
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