Advancing Foundational ModelsEnterprise AIAI Agentsthrough Empirical Research
AfterQuery is a research lab investigating the boundaries of AI capabilities
Core Research Initiatives
Expanding AI capabilities through systematic investigation
Model Performance Boundaries
Identifying and transcending AI's current limitations
Reasoning Limitations
Investigating complex reasoning failures in current foundation models
Knowledge Representation
Exploring how domain expertise can be effectively encoded in model parameters
Context Processing
Researching attention mechanisms and their impact on specialized task performance
Human Expertise Capture
Mapping expert knowledge for enhanced understanding
Expert Decision Pathways
Documenting problem-solving approaches across specialized domains
Tool Interaction Patterns
Analyzing expert usage of on-the-job applications and tooling at millisecond precision
Tacit Knowledge Extraction
Developing methodologies to capture unwritten expertise from practitioners
Data Quality Dimensions
Quantifying the attributes of performance-enhancing data
Specificity Metrics
Quantifying the impact of domain-specific training examples on model performance
Contextual Richness
Measuring the relationship between example complexity and performance improvements
Targeted Diversity
Researching optimal variation patterns within specialized training datasets
Research Methodology
Our systematic research approach to building high-quality training datasets
Gap Identification
Empirical testing to identify specific performance deficiencies in current models.
Research Design
Development of specialized data collection frameworks targeting identified gaps.
Expert Network Deployment
We activate our network of domain specialists to generate high-quality, real-world insights and examples.
Quality Assurance
Every data point undergoes rigorous validation, cleaning, and enrichment while preserving critical context and metadata.
Model Integration
Creation of production-ready datasets, formatted to custom specifications and ready to enhance model performance.
Or Ask Us About Our Research and Dataset Repository
Explore our library of previously developed datasets from past research initiatives
Research Philosophy
Guiding principles of our research methodology
Empirical Iteration
Our research embraces rapid hypothesis testing and continuous refinement, prioritizing methodical iteration on findings over single interventions
Human Expertise Primacy
We hold that human-generated data contains cognitive patterns and expertise that cannot be replicated through synthetic generation or web scraping
Practitioner Verification
We maintain rigorous standards for domain experts, ensuring validation by individuals with demonstrated field expertise
Adaptive Methodology
Our approach scales dynamically to address both targeted capability gaps and broader questions about AI functionality
Connect with our Team
Our research findings are advancing foundational model capabilities through human-generated, specialized datasets.