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
Six 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.
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 shipped feature
AI that understands your code.
Faster to get up to speed on a new project
An instant map of unfamiliar code.
Faster to find the code that matters
The right context, on demand.
Higher modeled annual return prior
Modeled return prior: 12.2% → 13.0% vs equal weighting.
Higher modeled service-level lift
Modeled service lift: 5.0 → 5.6 pp vs equal weighting.
Higher modeled carbon-reduction estimate
Modeled carbon reduction: 6.4 → 9.3 pp vs equal weighting.
Of the classification gap captured
Held-out AUROC averaged 84.8%.
Lower squared error than a mean-only predictor
Held-out R² averaged 49.8%.
More prediction error surfaced behind low-confidence warnings
12.5% more error when confidence was low.
Selected region to 50+ physical-unit measurements
71 ms median across 20 local runs.
CPU segmentation, no GPU required
16.4 s single run at 160×192×144 voxels.
Core measurement and safety test coverage
132 deterministic tests cleared the ≥85% coverage target.
Less first-pass review surface
74 bullets narrowed to 16 review targets.
Larger seed-eligible pool
Age 18–75 → 18–80 added 31 per 1,000.
Unsafe semantic mutation interception
18/18 unsafe blocked; 7/7 valid passed.
More correct rollout decisions
Correct SHIP decisions: 47.2% → 88.4%.
Honest targeting detection
Correct TARGET: 79.8%; 17.4% deferred to EXPLORE.
Lower decision regret
Missed value: 0.6 → 0.1 units per visitor.
Evidence reported in CA Code Graph. Representative product outcomes from the Code Graph research release.
White papers
The architecture behind trustworthy AI.
Six deep, practical papers: one per area of focus. Browse and filter them on the research page, and read any in full, no gate.