Maximizing human potential with AI
From Today's Research
To Tomorrow's Innovation.We publish, prototype and ship research across domains, translating advances in AI into systems that work in the real world.
Supercharging human creativity
We believe the most impactful AI research shouldn't stay locked in a lab. We prototype rapidly, publish openly, and ship thoughtfully across domains - turning meaningful advances in artificial intelligence into well-engineered systems that are genuinely built for the real world.
Areas of focus
Eight frontiers we're advancing.
Software Engineering
Code-graph intelligence that gives AI coding agents honest, relational context: extracted by deterministic static analysis and served over MCP.
Logistics & Supply
Risk-calibrated forecasting and constrained capital allocation for connected logistics networks, with deterministic scenario comparisons across growth, resilience, and decarbonization.
Biotechnology & Life Sciences
Molecule-level ADMET triage and protocol-to-predicate trial feasibility that separate predicted signal from configurable policy, so liabilities and enrolment constraints surface before commitment.
Digital Pathology
Measurement-grounded imaging dialogue that computes traceable quantities first and blocks diagnostic, grading, and prognostic claims before any narration is written.
E-commerce & Retail
Value-per-randomized-unit experiment decisioning that exposes conversion-value disagreement and permits shipping or targeting only when observed uncertainty gates pass.
Financial Compliance
Fraud adjudication that scores signal against explicit policy thresholds and carries evidence, provenance, and reviewer rationale into every escalation it raises.
Quick Commerce
Basket-value-at-risk planning that couples assortment, depth, substitution, and stockout cost so constrained dark-store slots protect whole baskets, not isolated SKU margin.
Food Delivery
Regret-weighted promise and dispatch decisions that model censored ready times, courier capacity, refund risk, and order fragility instead of optimizing point-ETA accuracy.
Impact
The superpower your engineers always wanted.
Precise context, compounding returns.
When an AI assistant can see how all the pieces of your software fit together, work moves faster and breaks less often.
Less time from idea to feature
Faster onboarding to a new project
Faster code discovery
Higher modeled annual-return prior
Higher modeled service-level lift
Higher modeled carbon reduction
Classification gap captured
Lower error than a mean-only predictor
More error surfaced at low confidence
Selected region to 50+ measurements
CPU segmentation, no GPU
Core measurement & safety coverage
Less first-pass review
Larger seed-eligible pool
Unsafe semantic mutations blocked
More correct rollout decisions
Honest targeting detection
Lower decision regret
Fewer abandoned baskets
Lower abandoned-order margin loss
Higher fill rate
Courier capacity recovered
Honest ready-time estimates
Fewer refund-threshold breaches
Evidence reported in CA Code Graph. Representative product outcomes from the Code Graph research release.
White papers
The architecture behind trustworthy AI.
Eight deep, practical papers: one per area of focus. Browse and filter them on the research page, and read any in full, no gate.