From delivery routing to power-grid scheduling, Busleyden finds near-optimal solutions in real time—no tuning, no repair.
Get early access to the world's fastest optimization engine
Busleyden turns reactive, brittle systems into proactive, self-tuning platforms, enabling real-time decision-making that adapts instantly to changing conditions.
Whether you're running auctions, cloud autoscalers, or robot fleets, Busleyden adapts in real time. Organizations gain a competitive edge: massive efficiency gains, faster response times, and the ability to solve previously impossible optimization problems in real-time.
| Domain | Baseline Solver | Our Engine | Speedup |
|---|---|---|---|
| Power-Grid SCUC | 300 ms / solve | 15 ms / solve | 20× |
| Ad Bundling | 400 ms / solve | 8 ms / solve | 50× |
| Delivery Routing | 150 ms / solve | 5 ms / solve | 30× |
Before claiming GPU speedups, we delivered a bullet-proof, CPU-only baseline that's 80× faster than Kissat 3.1 on SAT Competition 2023 "hard track" instances.
Reproducible harness running MiniSAT 2.2, Glucose 4.2, CaDiCaL 2.0, Kissat 3.1 with median & MAD stats over ≥30 seeds per instance.
Watch Busleyden solve a complex power grid optimization problem in real-time. Traditional methods take seconds and leave violations—we solve it in milliseconds with zero violations.
Interactive demo • No setup required
Click "Run Live Demo" to see Busleyden solve the IEEE 118-bus test case in real-time
Traditional SCUC+ACOPF methods leave 98 bus violations (17 pu on 4,000 MW load) requiring expensive repair. Financial penalties for out-of-limit voltages run $500–$2,000 per MW-hour—potentially $20,000–$70,000 per hour in penalties before repair.
Busleyden's unified approach eliminates violations entirely while achieving 4.8% better objective value, delivering $6,500+ hourly operational savings plus avoiding all penalty costs.
Microsecond latency arbitrage with portfolio optimization. 4,000× speedup enables real-time rebalancing in $12B+ algorithmic trading markets.
Sub-millisecond auto-scaling decisions for continuous resource allocation vs discrete scheduling in $500B+ cloud computing.
Real-time adaptation vs static puzzle solving. Dynamic difficulty adjustment and procedural content generation for $200B+ gaming industry.
Continuous optimization vs discrete combinatorics. Differentiable optimization enables gradient-based learning for $100B+ ML infrastructure.
Real-time routing vs static scheduling. Handles uncertainty and dynamic changes in $40B+ logistics optimization.
Hybrid discrete/continuous decisions for robotics and control systems. Enables trustable "copilot" systems in critical operations.
pip install busleyden
from busleyden import Engine
result = Engine.solve(problem_config)Any optimization problem can be encoded as a simple JSON configuration. No domain-specific knowledge required.
Unifies discrete logic, continuous optimization, linear algebra, and complex analysis into a single mathematical framework.
Any combinatorial or continuous decision problem encoded as energy minimization over high-dimensional complex vectors.
Massive vector operations on commodity CPUs/GPUs. No specialized hardware required—runs on standard laptops.
Dynamic re-optimization under changing conditions. Handles millions of constraints/variables seamlessly.
Be among the first to experience the future of optimization. Get early access to Busleyden's hyper-vector engine.