Capability
Data Infrastructure
High-quality data pipelines for AI training, including structured datasets, human-in-the-loop systems, and domain-specific collection.
Open Gates
AI data infrastructure
Building the data infrastructure layer for real-world AI systems.
We work with AI labs and product teams to build high-quality data pipelines, human intelligence layers, and real-world environments for training and evaluation.
From data collection to evaluation, we support the full lifecycle of AI systems.
Operating Model
Data + human intelligence + evaluation
Primary Users
AI labs and product teams
Delivery Mode
Structured workflows with distributed operators
Platform Overview
Data Infrastructure
01Human Intelligence Layer
02Evaluation & Real-World Testing
03Infrastructure Stack
Node 01
Data pipelines for model training and iteration
Node 02
Human intelligence systems for review and quality control
Node 03
Evaluation environments that validate real-world performance
Built for
AI labs, product teams, and teams shipping real-world AI products.
Data production
Human intelligence
Evaluation systems
Proof Surface
Designed to hold customer marks, partner signals, and operating proof.
The layout below is built to hold logos, customer categories, or partner brands without changing the structure of the page.
AI Labs
Model Teams
Applied AI
Product Builders
Evaluation Ops
Foundation Models
3
Core operating layers
End-to-end
Coverage from collection to evaluation
Distributed
Execution model with operators and experts
What We Build
Three operating layers for teams building production AI systems.
We build the capability stack behind data production, human intelligence, and evaluation.
Capability
Human Intelligence Layer
Distributed annotation, review, and evaluator workflows operated with quality control and production discipline.
Capability
Evaluation & Real-World Testing
Task-based testing environments, agent evaluation workflows, and validation systems tied to real deployment needs.
What We Support
The workflows modern AI teams need to improve model quality.
Model training datasets
Human-in-the-loop workflows
Agent evaluation and benchmarking
Real-world task environments
Data quality and feedback loops
How We Work
Structured like an operating system, not an informal network.
Step 01
Design the workflow
We define the data, review, and evaluation architecture required for the target model or product.
Step 02
Operate the pipeline
We run the human and system layers needed to generate, review, and validate outputs at quality.
Step 03
Close the loop
We turn evaluation signals into feedback loops that improve coverage, reliability, and model behavior.
Delivery Loop
We operate through a distributed network of contributors, operators, and domain experts.
Node 01
Workflow design aligned to the target model or product
Node 02
Execution across data, review, and evaluation systems
Node 03
Feedback loops that improve quality over time
Network
A distributed operating network behind AI data and evaluation systems.
We operate through contributors, operators, evaluators, and domain experts rather than presenting as a loose community.
Operating roles
ContributorsActive
OperatorsActive
ReviewersActive
EvaluatorsActive
Domain expertsActive
Execution Surface
Node 01
Annotation and reviewer workflows
Node 02
Task execution and agent evaluation
Node 03
Specialized domain support
Node 04
Quality control across the pipeline
For training
Structured data pipelines for model improvement and iteration.
For evaluation
Task environments and reviewer systems that produce actionable signals.
For deployment
Feedback loops that help models perform under real operating conditions.
Contact
For AI data, evaluation, and infrastructure collaboration.
Typical engagements include data pipeline setup, evaluation workflows, and human-in-the-loop systems.