Expert-level optimization.
Zero headcount.
Agentic profiling, analysis, optimization, and continuous iteration. Your infrastructure gets the attention of a veteran performance engineer—only it never sleeps and learns from every deployment.
The Market Reality
Financial Services
Inference costs run $450k+ per month at major financial institutions. Risk models, fraud detection, and trading systems run 24/7 on NVIDIA infrastructure. Yet most teams don't have visibility into why their production workloads run 30-40% slower than vendor benchmarks. A 10% optimization translates to $1.5M in annual savings. The problem: no one owns the inference performance layer, so it compounds month over month.
Manufacturing
48% of manufacturers report difficulty filling high-skill GPU optimization roles. Quality control, predictive maintenance, and production planning increasingly depend on real-time inference. But the specialized knowledge to optimize these workloads is rare and expensive to hire. Most manufacturers lack internal expertise in GPU architecture, memory hierarchies, and kernel optimization. They're either overpaying for compute or accepting suboptimal performance as inevitable.
Defense & Autonomous
Defense applications require real-time inference in disconnected, disrupted, and limited (DDIL) environments where cloud offloading isn't an option. Every inference must run on-device, on-time, and correctly. Tesla and similar autonomous platforms must run dozens of neural networks in parallel on embedded GPUs—any inefficiency reduces safety margins or requires more expensive hardware. Performance optimization isn't optional; it's architectural.
How It Works
An AI agent runs in your staging environment with your data and traffic patterns. It profiles autonomously, identifies bottlenecks, proposes optimizations, tests them, validates accuracy, and iterates. What would take a team of performance engineers months happens in days.
The agent deploys in your staging environment and continuously profiles your inference using hardware-level metrics. Your data, your models, your traffic patterns—not synthetic benchmarks. No code changes required. The agent knows which metrics matter because it was architected by someone who has spent 25 years learning to read them.
Gemini's long context windows enable us to parse comprehensive NCU performance profiles in full, identifying architectural constraints with deep context. Is your workload memory-bound, compute-bound, cache-limited, bank-conflicted, or scheduling-constrained? The agent knows the difference because those distinctions are embedded in its reasoning. Different bottlenecks require different solutions—and the agent optimizes accordingly.
The agent autonomously proposes optimizations, applies them, measures performance and perplexity, validates accuracy, documents changes, and iterates. It stops when it hits saturation—no further gains found. The entire loop runs with no human in the middle, though you control the constraints: accuracy tolerance, memory budget, latency requirements.
For every proposed optimization, the agent thoroughly tests it and runs complete perplexity loss analysis. You see the full tradeoff surface: speed gains vs. accuracy cost. Some teams need zero accuracy loss; others can accept 2% degradation for 30% speedup. You decide which points matter. The agent optimizes within your boundaries, not against them.
Why This Matters
Who We Are
Sujatha Kashyap is a systems performance engineer with 25 years of experience at the hardware-software interface. She holds a Ph.D. in distributed systems and dozens of patents in systems architecture across memory, cache optimization, virtualization, and resource orchestration.
At IBM, she led performance optimization across POWER4 through POWER10, spending decades on post-silicon validation, memory hierarchy tuning, and cache optimization. At Meta, she architected network-on-chip performance for AR SoCs under extreme thermal and power constraints. She's worked with enterprise workloads for decades, solving the same fundamental problem across a variety of hardware and software stacks.
Across thousands of deployments and myriad workloads, she's identified the patterns. She knows what questions to ask, which metrics matter, when memory bandwidth is the constraint vs. when it's L2 cache efficiency. She's optimized for single-thread latency, aggregate throughput, SMT scheduling, NUMA effects, interconnect congestion.
From firsthand experience, she knows enterprises of all sizes leave millions on the table because they lack access to performance engineering expertise. She is encoding 25 years of pattern recognition into an agentic system that democratizes what she knows, to make deep performance optimization available to every organization, regardless of size or domain.
Let's Talk
Working across Austin, Bangalore, and Silicon Valley, we're seeking design partners to validate the agentic optimization approach across different industries and hardware setups.