Sieve GTM brain

Sieve supplies custom multimodal datasets and evaluation environments to AI labs and robotics teams training models for the real world.

category

Multimodal AI training data

market profile

  • text: The company shifted from video APIs to end-to-end datasets after customers used its tools to annotate research data, according to its 2026 founder update.
  • text: Physical AI developers need collection and annotation spanning egocentric video, demonstrations and synchronized sensor data; several vendors compete in that layer.
  • text: Former API pricing should not be treated as current dataset pricing; a third-party review describes custom purchase agreements.

personas

  • title: Head of multimodal model research
    pains:
    • Finding diverse, relevant audiovisual data for a particular model objective
    • Balancing dataset quality and speed at very large scale
    • Knowing whether labels and source material fit training requirements
  • title: Robotics data and evaluation lead
    pains:
    • Collecting first-person demonstrations with synchronized sensor streams
    • Testing policy performance across varying real-world tasks and conditions
  • title: AI data infrastructure lead
    pains:
    • Processing, filtering and indexing massive video libraries
    • Coordinating sourcing, quality assurance and annotation without delaying model training

icp

  • text: Frontier model labs and AI startups training video, audio, world models or multimodal interaction systems; headcount is not specified.
  • text: Robotics research and product teams requiring first-person demonstrations, bespoke capture or real-world and simulation evaluation.

message pillars

  • text: Capture first-person demonstrations and related sensor data.
  • text: Collect audiovisual data with tailored capture setups.
  • text: Evaluate robots across simulated and physical settings.
  • text: Provide synchronized video and audio for multimodal learning.

objections

  • objection: Will this data improve our specific model?
    response: Define the task, collection specifications and evaluation baseline, then inspect a sample before agreeing to a larger dataset.
  • objection: How can we trust coverage, rights and quality?
    response: Ask for source rights, sampling and QA evidence for the requested dataset; Sieve describes layered automated and human checks.
  • objection: How much will a dataset cost?
    response: Request a scoped quote based on the required modalities and annotations; no reliable current public starting price was found.

competitors

  • name: Toloka
    website: https://toloka.ai/robotics
    why: Collects and annotates multimodal training datasets for robotics teams.
  • name: Scale AI
    why: Provides physical AI data collection and multimodal annotation for robotics labs.
  • name: Appen
    why: Provides managed data services and collection for physical AI.
  • name: Innodata
    why: Offers custom robotics training data and egocentric video collection.

influencers

  • name: Mokshith Voodarla
    url: https://www.sieve.ai/blog/reintro
    why: Co-founder who explained the company's shift to multimodal dataset supply.
  • name: Talha Baig
    url: https://www.linkedin.com/in/talha-baig
    why: Research and product lead discussing customer-facing dataset curation.

keywords

  • multimodal training data
  • egocentric video dataset
  • robotics demonstration data
  • robot evaluation environments
  • custom video data collection
  • audiovisual training datasets
  • world model training data
  • sensor synchronized training data

voice

  • technical and research-oriented
  • precise
  • ambitious about real-world AI

signals

  • text: A model lab starts a video generation, audiovisual understanding or world-model training program and needs specialized data.
  • text: A robotics team is expanding human demonstration collection or real-world evaluation coverage.
  • text: A research team is investing in large-scale video indexing and filtering to identify training examples.

hiring

  • Reliability Engineer (YC listing; verify current availability)
  • Product Engineer (YC listing; verify current availability)

pricing

model: unknown

motion

sales_led

channels

  • research publications
  • LinkedIn
  • X
  • research community events
  • data requests

confidence

medium

Founders

  • name: Mokshith Voodarla
    title: Founder
    role: Founder
    bio: Mokshith is co-founder and CEO of Sieve. Mokshith used to work on computer-vision problems at Scale AI, NVIDIA, and Ford where he experienced first-hand, the difficulties of building and scaling vision-based analytics systems. Before that, he graduated from UC Berkeley with a B.S. Electrical Engineering and Computer Science. Mokshith found his love for computer-vision on his high school robotics team, where he also met Abhi. Mokshith deeply enjoys orange chicken.
  • name: Abhinav Ayalur
    title: Founder
    role: Founder
    bio: Abhinav (Abhi) Ayalur is the co-founder and CTO of Sieve. Abhi leads technical design and development along with steering the overall company vision. Abhi's previous experience includes stints in computer vision, data analysis, and API development at Second Spectrum, NVIDIA, Microsoft, and Niantic. Abhi graduated UC Berkeley with a B.S. in Electrical Engineering and Computer Science. In his spare time, Abhi loves to play the alto saxophone, eat good food, and play basketball.

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