AI Data Infrastructure

Traditional Model

Human-wave tactics + Excel + per-item billing

→

AI Native

Agent orchestration + human judgment + RL loop

01

Observe

Intelligence Scanning

Agent Scanning 93 HF organizations and 125 X accounts, tracking dataset trends and competitive dynamics

Human Deciding tracking direction, assessing intelligence value

02

Analyze

Reverse Analysis

Agent Reverse-engineering sample structures, auto-generating labeling specs, cost models, and replication plans

Human Reviewing plan feasibility, adjusting production parameters

03

Produce

Data Production

Agent Seed augmentation, template synthesis, batch production with precise cost calculation

Human Defining schemas, reviewing synthesis quality

04

Judge

Human Judgment

Agent Pre-labeling, task distribution, progress tracking

Human Core judgment — Is this answer good? Is this reasoning correct?

05

Verify

Quality Verification

Agent 9-rule automated checks, anomaly detection, model fingerprint auditing

Human Auditing whether the model has truly absorbed the data's value

06

Train

Training Loop

Agent Trajectory recording, reward computation, pipeline orchestration

Human Defining rubrics, tuning reward functions, evaluating training outcomes

Train output feeds back to Observe — continuous iteration

AntGather Community

Behind every Human tag in the Agent Pipeline is a structured expert judgment network.

Traditional Crowdsourcing

Mechanical labor pool + per-item billing + untraceable

→

AntGather Community

Expert network + capability model + signal purification

0

Judgment Nodes

Average task matching within 3 days

0

Professional Domains

From code reasoning to medical law

0

Bachelor's Degree or Above

Real practitioners, not generalist annotators

0

Avg. Age

AI-native generation, understands frontier models

TIER 1

Domain Expert

Providing Know-why tier judgment

Judge Defining Rubrics and designing evaluation criteria

Train Calibrating reward functions, evaluating training outcomes

Verify Auditing whether models have truly absorbed data value

PhD in Mathematics · Law Professor · Senior Architect

TIER 2

Skilled Annotator

Executing Know-how tier judgment

Judge Preference scoring, pairwise comparison, reasoning validation

Produce Reviewing synthesis quality, defining Schemas

Analyze Reviewing reverse-engineering plan feasibility

Full-stack Engineer · Product Manager · Data Analyst

TIER 3

Crowd Validator

Community Validator

Completing Know-what tier validation

Verify Multi-person cross-validation, consistency checks

Observe Intelligence value screening, boundary testing

Produce Data collection, foundational labeling

Graduate Student · Freelancer · Cross-domain Enthusiast

Signal Quality Assurance

A three-layer mechanism for purifying human signals from noise

01

Multi-person Cross-validation

The same task is distributed to multiple annotators independently. Consensus (Inter-Annotator Agreement) is calculated, and low-agreement samples automatically trigger expert review.

Image labeling agreement < 70% → Automatically escalated to computer vision expert review

02

Capability Model Calibration

Each judge maintains a domain capability vector, with weights dynamically updated based on historical performance. High-weight judges' signals receive higher confidence in Reward Model training.

In math reasoning tasks, PhD annotator weight ×1.8, undergraduate annotator ×1.0

03

Continuous Feedback Loop

Model training results feed back to the AntGather Community. When model performance degrades in a specific domain, corresponding labeling data is automatically traced back and signal sources are recalibrated.

Model accuracy drops in legal scenarios → Automatically traces recent labeling data in that domain

Intellectual Asset Ownership

A judge's contributions are not one-time expendables — they are traceable, cumulative intellectual assets

Traditional Model

Per-piece Consumption

Labeling ends once done
Contributions are untraceable
Labor is a one-time expenditure
Individual value is unmeasurable

AntGather Model

Intellectual Equity

Contributions traceable on-chain
Capability model accumulates over time
High-quality signals earn ongoing dividends
Attribution-bound, expert identity appreciates

Domain Coverage

A comprehensive coverage network of training data types × professional domains

0 MCP Tools

The entire infrastructure exposes 110 MCP endpoints.
Your Agent can directly call our capabilities.

Radar 19 · Recipe 12 · Synth 9 · Label 12 · Check 11 · Audit 8 · Gym 19 · Crew 20

Competitive Monitoring

Agents call Radar MCP to scan HuggingFace for new datasets and auto-generate weekly reports

Radar · Recipe

Data Quality Inspection

After uploading a dataset, Agents automatically run 9-rule checks and output a quality report

Check · Audit

Synthetic Production

Agents read schemas → call Synth for batch generation → Label distributes labeling tasks

Synth · Label

// claude_desktop_config.json
{
  "mcpServers": {
    "knowlyr-datacheck": {
      "command": "knowlyr-datacheck",
      "args": ["mcp"]
    }
  }
}