Human Intelligence for Artificial Intelligence: AI and LLM Solutions
Power your LLMs with the world’s largest deterministic dataset of explicit human preference, sentiment, and opinion data
Taste is Truth: Grounding AI in Human Judgment
1.5 Billion+ Explicit Preference Votes
100 Million+ Global Users
30 Million+ Entities Mapped
15+ Years of Deterministic Data
“The race for the best AI will be won by those with the highest quality human signal. Ranker is that signal.”
Explicit Human Judgment is the Missing Layer in Modern AI
AI doesn’t understand taste. Modern AI systems are incredibly powerful, but fundamentally probabilistic in subjective domains. They rely on inferred affinity proxies—guessing what users want based on language and behavior, and grounded in similarly limited training data. These signals are often ambiguous, biased, and unstable—especially in sparse data scenarios such as cold starts.
For Example
Millions of people watched Succession, and many enjoyed its wit and tragic family dynamics—but that does not mean they’ll love all shows about corporate politics.
A probabilistic system relies on the co-occurence of metadata tags like wealth, power, and business, leading it to repeatedly recommend campy finance soaps or dry procedural dramas. Without explicit preference signals, the AI cannot distinguish between a viewer who values prestige writing and one who simply likes stories about the ultra-rich. The result is overgeneralization caused by a system that understands the topic, but not the user’s unique taste.
AI doesn’t understand taste. Modern AI systems are incredibly powerful, but fundamentally probabilistic in subjective domains. They rely on inferred affinity proxies—guessing what users want based on language and behavior, and grounded in similarly limited training data. These signals are often ambiguous, biased, and unstable—especially in sparse data scenarios such as cold starts.
This reliance on inferred behavior creates massive blind spots. Systems repeatedly overgeneralize, conflating mere interaction with genuine affinity for a broader category. The result? AI agents that struggle to arbitrate between competing, subjective choices. They generate outputs that are technically plausible, but fundamentally misaligned with real human preference.
Ranker solves this by capturing how people actually judge, compare, and choose. We provide the deterministic, first-party ground truth that allows models to move beyond guessing what users might tolerate, to knowing exactly what they prefer. Ranker’s data psychographically captures highly nuanced relationships that cannot be explained purely by text association alone. We give your models the precise signal they need to reason about human judgment itself.
Why Ranker Data is Different
| Feature | Typical Text & Behavioral Data | Ranker's "Taste Graph" |
|---|---|---|
| Signal Fidelity | Ambiguous Proxies: Inferred from passive engagement and text co-occurrence. | Deterministic Signal: 1.5B+ explicit, verified human votes and comparative ranked choices. |
| Relational Logic | Probabilistic Association: Models learn that entities are related, but not which is better. | Ordinal Structure: Models learn relative value and hierarchy through ranked preference data. |
| Sentiment Type | Affirmative/Positive Signal: Often a reliance on noisy negative sampling. | Explicit & Bidirectional: Captures both active likes and active dislikes for high-fidelity alignment. |
| Data Density | Sparse & Fragmented: Significant gaps in relational understanding for niche or long-tail entities. | Taxonomically Dense: 15+ years of cross-category voting reduces sparsity in the "long tail." |
| Data Readiness | Manual Labeling Required: Needs costly, high-latency human-in-the-loop pipelines for reward signals. | Ready for RLHF/DPO: High-density preference labels built for instant reward model and pairwise alignment. |
| Data Provenance | Scraped/Second-Hand: High risk of bot noise, SEO spam, generative content, and questionable data provenance. | First-Party & Verified: Clean, structured data with 100% provenance and automated quality controls. |
Core AI, ML, and LLM Use Cases
Model Alignment & Preference Fine-Tuning
Fine-tune foundational models and align AI agents using Ranker’s explicit voting dataset. Our bidirectional signals (explicit likes and dislikes) provide clear, deterministic labels required for modern alignment techniques—including RLHF and Direct Preference Optimization (DPO)—drastically reducing the need for costly, bespoke human labeling pipelines.
Recommender Systems & Personalization
Enhance deep recommendation and ranking models by incorporating explicit preference signals into your training and evaluation workflows. Ranker’s structured choices help models distinguish true preference from exposure-driven behavior, solve cold-start problems in the absence of user history, and reduce the overgeneralization common in standard engagement-based systems.
Agentic Context (MCP & RAG Integrations)
Connect your agents directly to the "Taste Graph." Using Model Context Protocol (MCP) or standard RAG APIs, enable your systems to retrieve up-to-date preference insights at inference time. Give your agents the real-time context needed to accurately answer subjective queries and adapt to cultural shifts without retraining.
Embedding Training & Knowledge Graphs
Train or refine embeddings that encode not only semantic similarity, but relative comparative value. Ranker’s ordinal rankings introduce a comparative structure that shapes your embedding spaces according to how people actually prioritize and compare entities across 250,000+ topically framed categories.
Audience Segmentation & Clustering
Develop advanced audience segments based on shared preference patterns rather than inferred behavior or basic demographics. Ranker enables data science teams to expand audience reach while maintaining precision, supporting highly targeted personalization and discovery models grounded in actual, shared taste.
Benchmarking & Ground Truth
Evaluating an AI’s output in subjective domains is notoriously difficult. Use Ranker’s millions of ordinal data points as a reliable ground truth to evaluate how well your model understands human culture and consensus—from "Underrated Sci-Fi Movies Where It's Best Going In Blind" to "The Most Influential Starting Levels In Video Game History" When models generate competing responses, our data provides the stable reference point needed for model arbitration and to align responses with real human judgment.
Data Delivery & Technical Integration
Ranker provides flexible delivery models designed to integrate seamlessly into existing AI, ML, and data science workflows—without disrupting your established pipelines.
Model Context Protocol (MCP) & Real-Time APIs
Connect your autonomous agents and LLMs directly to the "Taste Graph." Our low-latency APIs and MCP servers enable real-time retrieval of contextual preferences, trending sentiment, and knowledge graph entities for RAG-based inference and live agentic decision-making.
High-Density Bulk Datasets
Available in Parquet, JSON, and CSV for large-scale pre-training, fine-tuning, and model alignment. Our structured datasets include explicit vote tallies, bidirectional sentiment (likes/dislikes), and ordinal rankings across 30M+ entities, ready for ingestion into your vector stores or data lakes.
Pre-Trained Preference Embeddings
Accelerate development with off-the-shelf vector representations derived from Ranker’s opinion graph. These embeddings are optimized for preference-aware tasks, capturing the relative comparative value and human-centric relationships that standard text-based embeddings miss.
The Opinion Graph (Custom Correlations)
Access deep, item-level relationships and cross-interest affinities. Our custom correlation reports provide psychographic insights into cross-category behavior (e.g., the specific link between consumer brand loyalty and entertainment preferences) to build more nuanced predictive models.
Cloud-Native Pipeline Integration
Ranker data is delivery-ready for enterprise environments. We support automated data delivery pipelines directly to AWS S3, Google Cloud Storage, and more, ensuring a high-integrity, version-controlled data flow into your training environment.
Proof Points: Proven at Scale
Powering Personalization and Discovery
Ranker’s preference data is the engine behind Watchworthy, the leading TV and movie recommendation platform (available on Apple iOS and Android). In head-to-head independent testing, Watchworthy’s preference-driven engine generated significantly higher engagement than leading OEM and algorithmic solutions.
Audience Intelligence for Higher-Performance Campaigns
Ranker Insights leverages our deep psychographic data to support high-value advertising campaigns. Our massive privacy-first, first-party entertainment graph of 100+ million people enables us to precisely develop sophisticated audience clusters based on deterministic correlations and real affinity.
ComScore Top U.S. Digital Media Property
Ranker is a recognized leader in digital media, ranked by ComScore #20 (Q1 2025).
15 Years of Temporal Depth
Unlike static web-scraped datasets that offer a mere snapshot in time, Ranker provides over a decade and a half of consistent, longitudinal data collection. This allows models to understand not just what people like today, but how human cultural consensus and "taste" evolve over time.
Deterministic Signal Purity
Ranker employs automated data quality controls and rigorous auditing mechanisms to detect and flag suspicious voting patterns. Because voting is anonymous and topically framed, our dataset reduces "social confirmation bias," providing a more authentic, non-performative measurement of actual human preference than social media or review-based text.
Global & Demographic Breadth
Our data reflects a diverse, global audience of 100M+ users with a demographic profile that mirrors the general population. Ranker provides the massive scale required to capture macro-cultural trends, while maintaining the precision and depth necessary to model constituent audiences and unique long-tail subgroups.
Ready to align your model with the world's actual preferences?
Connect with us to Schedule a Demo or Request a Data Sample