Our story
About Bhoomi
Behavioral & Hyperlocal Open-source On-ground data gathering MachIne
The Bhoomi Initiative is the research-aligned home for that mission: a platform focused on underrepresented communities, languages, and regions. We help teams and volunteers create high-signal training data where generic corpora fall short so the next generation of models is fairer, more accurate, and actually useful in the real world.
Behavioral & Hyperlocal Open-source On-ground data gathering MachIne
Six letters in “Bhoomi” map to this phrase from behavioral signal through on-ground gathering to the open machine that makes work actionable.
BHOOMI
Multi-language workflows
Structured metrics & history
Built for workshops & teams
The acronym
What “Bhoomi” spells out
Each letter reflects a commitment behind the platform from how we treat behavior and locality to how open, on-ground work feeds the pipeline we call the machine.
B
Behavioral
Human judgment, norms, and choices the signal we capture is behavioral, not only textual.
H
Hyperlocal
Ground-truth reflecting specific languages, regions, and communities not a single global average.
O
Open-source
Transparent tooling and workflows others can inspect, fork, and improve with the community.
O
On-ground
Data collection tied to real contexts and participants, not only distant or synthetic sources.
M
Machine
on-ground data gathering feeds an open pipeline ingestion, metrics, and exports that turns participation into training-ready data.
I
Machine
It aligns with the same word: insight, integrity, and iteration stay explicit in the systems we build not buried in a black box.
“Better AI starts when the people who are affected by models get to shape what those models learn.”
Bhoomi Initiative
What we do
Why this platform exists
We connect contributors with a guided pipeline: from defining a research topic to reviewing model output and locking in high-quality labels you can trust downstream.
Grounded in place
Topics and rationales are shaped by regional and linguistic context not generic defaults so models learn what communities actually care about.
Humans in the loop
Reviewers review AI-generated rationales and answers, correct bias, and improve quality with clear metrics and transparent workflows.
Open iteration
We treat reviewing as collaboration: refine questions, challenge weak answers, and leave a traceable history of how judgments evolved.
Principles
How we work together
Simple norms that keep reviewing humane and outcomes trustworthy.
Respect & clarity
Guidelines and UI copy stay plain and respectful no jargon walls between contributors and the work.
Rigorous but fair
Metric-based scoring keeps quality measurable while allowing nuance for cultural and topical edge cases.
Impact over volume
We care about useful, representative data not racing to the largest raw count of low-signal labels.
Join the effort
Ready to contribute or partner?
Whether you are reviewing solo, running a workshop, or exploring research collaboration we would love to hear from you.
Built for diverse voices one review at a time.