Labs
Outcomes over experiments.
The technical landscape is noisy. New frameworks every week, papers that never leave the lab, benchmarks that don't survive contact with real data. We cut through it. We only pursue research that solves an actual business problem for an actual team.
Every architecture, model, and system we build is measured by one thing: did it move the needle? Revenue, efficiency, reliability, speed to market — that's the benchmark. If it doesn't produce a result a founder can point to, it doesn't ship.
Current Research
- Bayesian reasoning under uncertainty Posterior inference and probabilistic graphical models for decision-making where data is sparse and stakes are high
- RLHF & human-in-the-loop optimization Reward modeling, preference learning, and online policy refinement with structured human feedback loops
- Automated evaluation & validation pipelines Model-graded assessments, regression harnesses, and continuous validation gates integrated into CI/CD for ML
- Causal inference & counterfactual modeling Structural causal models, do-calculus, and interventional analysis to isolate true drivers from confounders
- Online learning & adaptive control systems Multi-armed bandits, contextual policies, and real-time parameter adaptation under non-stationary distributions
- Compound AI & multi-agent architectures Orchestrating LLMs, retrieval, tool-use, and specialized models into verifiable end-to-end agentic systems
- Scalable data infrastructure research Columnar storage optimization, query engine internals, and next-generation lakehouse architectures for petabyte-scale workloads
- Distributed systems & fault-tolerant inference Consensus protocols, sharded serving, and graceful degradation patterns for low-latency ML at scale