Single-click training cluster orchestration on GKE and Slurm. Get automated profiling insights and AI architecture recommendations for MoE, checkpointing, and batching to eliminate GPU/TPU wastage.
India's optimized inferencing platform powered by TPU and GPU. Deploy with TorchTPU, bring your own models, and leverage custom XLA kernels for high-throughput, low-latency serving.
Aggressively localize compute capacity and chip supply chains. Deploy secure, production-grade AI with complete data ownership, bridging the performance gap with specialized architectures.
Hardware-software co-design optimized across the full accelerator spectrum — from silicon to serving.
Decoupling compute from memory to dynamically scale resources, minimizing latency and maximizing throughput for generative workloads.
Hand-optimized XLA and CUDA kernels designed to bypass standard framework overheads and squeeze maximum FLOPs from the silicon.
Advanced look-ahead prediction algorithms that draft tokens rapidly, verifying them in parallel to radically accelerate inferencing speed.
State-of-the-art tensor and pipeline parallelism strategies, distributing massive models across clusters with near-zero communication bottleneck.
Lossless precision reduction techniques (FP8/INT8) that dramatically lower memory bandwidth requirements without compromising intelligence.
Intelligent, heterogeneous cluster management that routes workloads to the optimal accelerator, dynamically balancing cost and performance.
Deep integration with MaxText, MaxDiffusion, and Pallas — Google's native TPU scaling stack — for uncompromising performance on XLA hardware.
Most kernel and training code is written once and never revisited — leaving significant performance on the table. We leverage AlphaEvolve, Google DeepMind's evolutionary AI coding agent, to continuously discover and apply superior algorithmic mutations to our infrastructure stack.
AlphaEvolve takes our seed kernels, defines a performance scoring function, and runs thousands of Gemini-powered mutations — keeping only the fittest. The result: training and inferencing code that no human team could hand-optimize alone.
Prioritizing deep understanding of real-world user struggles over building tech for tech's sake.
Fostering psychological safety and prioritizing human skills to collaboratively solve the hardest engineering problems.
Treating every challenge as a privilege to build profound, lasting impact with the utmost integrity.