Now in private beta

Real-world data
for agentic systems

Agents are powerful when information is digital. Emetry fills the gap — acquiring, verifying, and returning the real-world data your agents can't reach on their own.

Agents hit a wall at the
edge of the internet

The information your agent needs is often physical, unpublished, ambiguous, or simply not online. When the world can't self-report cleanly, agents stall.

Missing data

The fact your agent needs doesn't exist in any database, API, or webpage. It requires someone to go look.

Physical evidence

Photos, on-site observations, barcode scans — things that require a human with a phone at a specific location.

Trust gap

Even when data exists, agents can't assess if it's current, authentic, or matches the original ask without structured verification.

Request, acquire, verify, return

A single API call triggers a real-world information pipeline.

1

Agent submits a claim

Your agent describes what it needs verified — a product on a shelf, a building condition, a document at a specific address. Attach a bounty to incentivize fast resolution.

2

Emetry routes to nearby contributors

Geo-matched push notifications reach verified contributors in the area. The first to accept picks up the task and heads to the location.

3

Evidence is captured on-site

Photos, barcode scans, text recognition, GPS-stamped observations. All captured through the Emetry mobile app with device trust and location verification.

4

Validated result returned to your agent

LLM-powered validation scores the evidence against the original claim. Your agent receives a structured result with confidence scores, provenance chain, and the raw evidence.

Two connected sides

A marketplace that connects agentic systems to a global network of human contributors.

Contributor network

A marketplace of verified contributors who gather evidence, capture observations, and validate real-world facts on demand. Paid per task via the bounty system.

Agent control plane

REST APIs and orchestration for routing tasks, managing follow-up loops, scoring contributor trust, and returning machine-readable verified results.

Verification-first

Start with bounded, objective asks — confirm, validate, disambiguate — where quality is measurable and trust is earnable through consistent performance.

Structured provenance

Every result carries confidence scores, contributor reputation, device trust signals, GPS verification, and a full evidence chain your systems can evaluate.

create_claim.py
import emetry

client = emetry.Client(api_key="em_live_...")

# Your agent needs ground truth
claim = client.claims.create(
    title="Verify book on shelf",
    description="Check if ISBN 978-0-13-468599-1 is
    available at Brooklyn Public Library",
    claim_type="photo",
    location=(40.6782, -73.9442),
    bounty=5.00,
)

# Poll or use webhooks
result = client.claims.wait(claim.id)

print(result.verdict)       # "confirmed"
print(result.confidence)    # 0.94
print(result.evidence[0])  # EvidencePhoto(...)

For developers

One API call to the physical world

Submit claims via REST or the Python SDK. Emetry handles contributor matching, evidence collection, LLM validation, and payment settlement.

  • REST API with webhook callbacks
  • Python SDK with async support
  • Photo, document, and observation claim types
  • Geo-fenced contributor matching via PostGIS
  • LLM-powered evidence validation
  • Structured results with confidence scoring

Bridge the physical gap

Give your agents access to the information that only exists in the real world.