AI DATA SOLUTIONS
Human Intelligence Layer
AI-Native Multilingual Human Intelligence for Model Training, Evaluation, and Governance
The Human Intelligence Layer is LILT’s AI-native system for sourcing, calibrating, and deploying human judgment at scale. It combines machine learning–driven assessment, agentic task routing, and expert-designed evaluation frameworks to produce reliable human signals for training, evaluation, and governance of advanced AI systems.
WHAT LILT MAKES POSSIBLE
Designed as an Intelligence System — Not a Workforce
Rather than treating humans as static resources, LILT models human expertise as a dynamic, measurable input — continuously assessed, recalibrated, and optimized for specific tasks, domains, and risk profiles.
All systems are designed and maintained by ML PhDs and applied research teams, grounded in empirical evaluation rather than heuristic workforce management.
Rapid, Model-Driven Expertise Assessment
Human capability is evaluated through rapid, task-specific assessments that measure domain knowledge, reasoning ability, and skill relevance in context — not through static resumes or generic tests.
These assessments enable:
• Fine-grained expertise differentiation
• Task-level suitability scoring
• Faster onboarding without quality dilution
EXPERTISE IS INFERRED FROM PERFORMANCE SIGNALS, NOT SELF-REPORTED CREDENTIALS.
Agentic Talent Discovery & Routing
LILT uses agentic systems to continuously identify, qualify, and route contributors to the work they are most likely to perform reliably.
This allows the Human Intelligence Layer to:
• Adapt to new task types and domains quickly
• Match contributors dynamically as requirements evolve
• Reduce variance caused by manual assignment
THE RESULT IS FASTER ITERATION WITH CONTROLLED RISK, EVEN AS TASK COMPLEXITY INCREASES.
Role-Aware Human Judgment
Human input is structured into explicit roles — such as evaluator, preference rater, reviewer, or safety assessor — each with defined expectations, permissions, and review thresholds.
Human judgment becomes contextual and constrained, not subjective or free-form.
Continuous Calibration & Signal Monitoring
Human outputs are continuously evaluated using observability frameworks and tools.
These mechanisms ensure that human signals remain stable, interpretable, and suitable for model training and evaluation, even as scale increases.
Disagreement is treated as diagnostic data, not failure.
HOW ITS DONE
Built by Research, Operated by Systems
The Human Intelligence Layer is designed by machine learning researchers and applied scientists with deep experience in:
Model evaluation- Human-Al interaction
- Measurement theory
- Multilingual and cross-cultural systems
Design decisions prioritize:
- Signal fidelity over throughput
- Measurement over intuition
- Repeatability over anecdote
This research-first approach ensures the system evolves alongside modern AI architectures — including agentic and multimodal systems.
WHAT LILT MAKES POSSIBLE
Embedded Across the LILT Platform
This research-first approach ensures the system evolves alongside modern AI architectures — including agentic and multimodal systems.
Training data generation (SFT, RLHF/RLAIF, RLVR)
Model evaluation and diagnostics
Localization and content validation
Safety testing and red teaming
Governance and compliance workflows
Foundation for AI That Works in the Real World
As AI systems become more autonomous, multilingual, and culturally embedded, human intelligence must be engineered with the same rigor as models themselves.
That is what the Human Intelligence Layer provides.