Command AGIAlign agents to human judgment

Capture, version, and deploy human judgment at scale. Train reward models, orchestrate annotation pipelines, and align AI agents to human preferences.

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The Problem

AI generates. Humans curate.

Generative models produce unlimited output, but aligning that output with specific aesthetic standards still requires human judgment. We make that judgment programmable.

Structured Elicitation

Multi-modal preference capture through labeled references and pairwise comparisons. Turn subjective judgment into structured data.

Git-like Version Control

Branch, commit, and merge preference profiles. Explore variations without losing progress. Diff and rollback changes.

REST API Evaluation

Score any image against a preference profile via API. Sub-100ms latency. Filter, rank, and route content programmatically.

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Process

How it works

Three steps from raw preferences to production API

workflow
1

Define

Label reference images, answer preference questions, and compare pairs to build a latent preference model.

2

Refine

Branch to explore alternatives. Commit iterations. Merge inputs from multiple stakeholders or annotators.

3

Deploy

Query your profile via REST API. Score images in milliseconds. Integrate into any pipeline or product.

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Use Cases

Who it's for

Any team that needs to encode subjective quality standards into software

Video & Post-Production

  • Automate frame selection against brand guidelines
  • Score B-roll and selects programmatically
  • Codify an editor's visual sensibility for downstream tools

AI Art & Generative Design

  • Filter AI generations by aesthetic alignment
  • Build preference-conditioned diffusion pipelines
  • Personalize creative tools to individual users

Content Platforms & Feeds

  • Build preference-aware recommendation systems
  • Curate feeds by visual quality, not just engagement
  • Match creators with aesthetically-aligned audiences

Research & Annotation

  • Collect diverse aesthetic preferences at scale
  • Build demographically-segmented preference models
  • Track how preferences evolve across populations
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Annotation Marketplace

Scale your annotation pipeline

Our marketplace connects you with professional annotators who label frames according to your criteria. Quality-controlled with agreement scoring and demographic targeting for diverse perspectives.

  • Pay per annotation with transparent, per-unit pricing
  • Built-in quality control via inter-annotator agreement
  • Demographic targeting for representative preference data
Start collecting data

Label a frame

Binary classification

Compare two frames

Pairwise preference

Platform fee

Configurable per project

20%

Get Started

Ready to make preferences programmable?

Create your first preference profile and start evaluating content in minutes.

Get Started Free