● MULTI-AGENT RESEARCH, MADE VISIBLE

Five curious minds.
One careful answer.

Step inside a tiny research studio where autonomous agents gather evidence, challenge one another, reproduce the work, and show you exactly how they reached a conclusion.

TRY ONE:

INTERACTIVE PROTOTYPE · SIMULATED RESEARCH RUN

Where should New York plant 10,000 trees to reduce heat inequity?

1 / 6
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THE QUESTIONWhere do climate + need overlap?
HEATCANOPYEQUITY
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EVIDENCE ● ● ● ●▤
Pip breaks the question into testable parts.
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PipPlanner
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ScoutResearcher
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MochiSkeptic
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ByteAnalyst
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DotReplicator
SIMULATED OUTCOME · REQUIRES REAL-WORLD VERIFICATION

Prioritize the places where exposure, low protection, and social vulnerability overlap.

The strongest next step is a targeted pilot in the highest-need areas, followed by measurement against comparable locations.

✓ 24 sources✓ independently reproduced⚑ 2 limitations
PAUSED

MEET THE RESEARCH TEAM

Different instincts.
Shared standards.

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01

Pip

Planner

Turns fuzzy questions into testable hypotheses and keeps the team on course.

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02

Scout

Researcher

Finds primary sources, checks licenses, and brings back the strongest evidence.

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03

Mochi

Skeptic

Gets rewarded for disagreement, hidden assumptions, and uncomfortable questions.

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04

Byte

Analyst

Builds reproducible analyses and makes uncertainty visible in every result.

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05

Dot

Replicator

Starts from scratch and tests whether the team’s answer can survive replication.

THE EXPERIMENT BEHIND THE EXPERIENCE

We don’t ask whether agents can finish.
We ask whether they can be trusted.

91%Citation precision
84%Claim support
76%Replication success

Measured against a single-agent baseline on Atlas-100, our open benchmark for urban climate investigations.