Correlation Factor provides AI consulting and advisory services for organizations looking to design, evaluate, and deploy intelligent systems grounded in sound mathematics, statistics, and applied research
Guidance on problem formulation, feasibility assessment, and architecture design for AI-driven systems.
Independent analysis of model performance, error patterns, data quality, and reliability.
Improving outcomes through data quality, labeling strategies, and distribution analysis.
Support for research teams working on advanced machine learning, multimodal systems, and experimental AI workflows.
Assessing robustness, scalability, and operational risks before deployment.
We design and evaluate retrieval-augmented generation systems that combine large language models with external knowledge sources. Our focus is on retrieval quality, chunking strategies, embedding selection, and grounding responses to reduce hallucinations and improve reliability.
We advise on the design of agent-based AI systems that plan, reason, and act across tools and workflows. This includes agent architecture, memory design, tool selection, and evaluation of multi-step reasoning behavior in real-world environments.
We help organizations select appropriate models based on task complexity, latency constraints, cost, and data characteristics. Our approach emphasizes empirical evaluation, benchmarking, and trade-off analysis rather than one-size-fits-all recommendations.
We provide guidance on model compression techniques such as quantization and pruning to enable efficient deployment. This includes accuracy–performance trade-offs, hardware constraints, and suitability for edge or resource-limited environments.
We advise on fine-tuning strategies including supervised fine-tuning, instruction tuning, and parameter-efficient methods. Our focus is on data quality, overfitting risks, evaluation protocols, and maintaining model generalization.
We consult on inference pipelines, batching strategies, latency optimization, and scaling considerations. This includes choosing appropriate runtimes, handling concurrency, and designing cost-efficient inference workflows.
We work extensively with open-source models and tooling. Our consulting covers model selection, licensing considerations, performance evaluation, and long-term maintainability when adopting open-source AI in production systems.
We help teams move models from experimentation to production by addressing monitoring, failure modes, robustness, and operational risk. This includes deployment architecture, versioning, rollback strategies, and observability.