Human Archive

Roadmap & Position in AI Data Infrastructure

Captures 8,000 hours/day of multimodal human activity data to train the next generation of robots.

Company Overview

Builds and operates large-scale, multimodal data collection infrastructure capturing synchronized video, depth, tactile, and motion data from real-world human activities to provide high-fidelity training datasets for robotics and embodied AI companies.

What They're Building

The company's public product roadmap & what they're committed to building.

Human Archive has publicly announced scaling multimodal data capture operations across diverse real-world environments (homes, restaurants, hotels, retail, construction, horticulture, industrial settings) in India, with capacity of up to 8,000 hours of annotated data per day. Sensor suite includes egocentric RGB, stereo depth, tactile, IMU, and wrist cam. Building the largest multimodal manual labor dataset for robotics foundation models.

Latest Intelligence

Zeitgeist tracks private signals to determine where the company is heading strategically.

Manual Labor Becomes Robotics Training Data

May 13, 2026

Confidence:

High

New Intel: Human Archive is capturing up to 8,000 hours a day of multimodal human activity data for robotics labs. The company is positioned as the data supply chain feeding embodied AI labs and robotics foundation models.

Founder and Key Execs

Rushil Agarwal, Co-Founder

Co-Founder

Samay Maini, Co-Founder

Co-Founder

Shloke Patel, Co-Founder

Co-Founder

Raj Patel, Co-Founder

Co-Founder

Founder Force Multiplier

Human Archive's massive, operationalized contributor network of 50,000+ real-world data collectors paired with custom multi-sensor hardware rigs deployed across dozens of environment types creates a proprietary, hard-to-replicate dataset moat for embodied AI that no competitor.

Funding History

2025 | Founded by Rushil Agarwal, Samay Maini, Shloke Patel, and Raj Patel.
2025 | $500K Pre-Seed from Y Combinator (W25 batch).
2025-2026 | Scaled to 50,000+ contributor network and 8,000 hours/day capture capacity.

Competitors

Data Labeling

Scale AI, Labelbox (general data labeling/annotation at scale).

Robotics Data

Open X-Embodiment (open-source consortium), Covariant (proprietary pipelines), Bridge Data / RT-2 datasets (academic).

Foundation Models

Skild AI, Physical Intelligence (π) (robotics foundation models with proprietary data).

Internal

Everyday Robots / Google DeepMind (internal robotics data collection).

Human Archive

's Moat:

50,000+ contributors wearing custom multi-sensor rigs across dozens of real-world environments. 8,000 hours/day of synchronized video, depth, and tactile data is a collection infrastructure that would take years and millions to replicate. Scale AI labels data; Human Archive captures it with proprietary hardware in environments that do not exist in simulation.

How They're Leveraging AI

Multimodal Robotics Data Curation

Providing large-scale, multimodal training datasets to robotics companies building foundation models for dexterous manipulation and physical interaction.

World Model Training Data

Supplying annotated, environment-diverse datasets to AI labs developing world models and scene understanding for embodied agents.

Robotics Model Benchmarking

Enabling robotics startups to benchmark and evaluate model performance across diverse manipulation tasks and environments using standardized, real-world datasets.

Tactile Sensing Data

Providing tactile and force-feedback datasets to companies developing dexterous robotic hands and haptic systems.

AI Use Overview:

Using multimodal robotics data curation with synchronized video/depth/tactile sensors, world model training pipelines, and tactile sensing data annotation.

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