Syntari

AI agents collecting data on every production-level robot in development, creating a database of archetypes for automated machines.

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The problem

The robotics industry is evolving faster than anyone can track. New models, new capabilities, new manufacturers every week. There's no comprehensive, structured database of what exists, what it can do, and how it compares to alternatives. Researchers and engineers spend hours manually collecting specs from press releases and product pages.

What we built

AI agents that continuously scan, collect, and structure data on every production-level robot in development worldwide. The data feeds into an archetype system that categorizes robots by capability, application domain, and maturity level.

First version built in 48 hours. The agents are always running, always collecting, always updating.

Screenshots

Features

What it does

AI data collection agents

Autonomous agents scanning manufacturer sites, papers, and news for robot data.

Archetype classification

Groups robots by capability and application domain, not just manufacturer.

Searchable catalog

Filtering, comparison, and detailed spec pages for every production-level robot.

Admin validation dashboard

Review and enrich AI-collected data before publication.

Maturity filtering

Distinguishes production-level robots from conceptual or prototype-stage entries.

Process

How we built it

Defined the data taxonomy

Created a structured schema for robot capabilities, specifications, manufacturers, and application domains.

Built the AI collection agents

Autonomous agents that scan manufacturer sites, research papers, and news sources for robot data.

Designed the archetype system

Classification framework that groups robots by what they can do, not just what they are.

Created the admin interface

Dashboard for reviewing, validating, and enriching the AI-collected data before publication.

Launched the public catalog

Searchable database with filtering, comparison, and detailed specification pages for each entry.

Reflections

What we took away from this project.

What went wrong

The AI agents initially collected too much noise. Press releases about robots that were conceptual, not production-level. We had to add maturity filters.

What went right

The archetype system resonated with researchers. Grouping robots by capability rather than manufacturer was a perspective shift they valued.

What we learned

AI data collection at scale needs human validation checkpoints. Autonomous doesn't mean unsupervised. The best results come from AI that flags uncertainty.

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