Deep-Tech AI Systems Engineering
AI performance, engineered below the surface.
We design, build and optimize AI systems from model architecture to GPU execution,
making them faster, more efficient and production-ready.
Proof
Measured production outcomes.
Every engagement is anchored in measurable performance: cost, throughput, latency, and bottleneck elimination.
20–60%
Inference cost reduction
2–5×
Throughput improvement
30%
Latency reduction vs cuBLAS baseline
Operating Where Performance Is
Operating at the level where AI meets hardware.
We optimize across the full stack — from model architecture to warp-level execution.
Our Clients
Capabilities
Operating at the level where AI meets hardware.
We work across the layers that determine how AI actually performs in production.
LLM Inference Systems
Prefill/decode, KV-cache, batching, speculative decoding, quantization.
Training & Model Architecture
Architecture–hardware co-design, multimodal systems, training stability.
CUDA Memory & Data Movement
Shared-memory tiling, async pipelines, register pressure, global memory traffic.
Warp-Level Engineering
Inline PTX, warp scheduling, low-precision compute, mma.sync pipelines.
Observability & Diagnostics
Microsecond visibility across CPU–GPU behavior and runtime bottlenecks.
System-Level Strategy
Cloud, air-gapped, sovereign, on-prem, and edge AI architectures.
Build With You / Build For You
Language models that fit reality.
We build SLMs and LLMs optimized for real workloads — co-developed with your team or delivered as production-ready systems.
Speech-to-Text for Indian languages
Speech-to-SQL and MangoQL systems
LLM/SLM architecture, serving, and deployment
Products & Systems Tooling
Systems we've built for hard AI problems.
View Products→Custom LLMs and SLMs built for real workloads, co-developed with your team or delivered as production-ready systems.
View Products→Book a Diagnostic →Training
Systems-first AI training for real infrastructure.
Built from real engineering work across AI systems,
GPUs, LLMs and production infrastructure.
Training
Systems-first AI training
for real infrastructure.
Built from real engineering work across AI systems, GPUs, LLMs and production infrastructure.
Programs — 01–05
01
System Architecture for AI
CPU/GPU execution, memory hierarchy, profiling, CUDA, Triton and heterogeneous systems.
Explore Program →02
Design & Development of LLMs
LLMOps, GPU infrastructure, PyTorch internals, distributed inference, and inference at scale.
Explore Program →03
System Optimization for LLMs
SM architecture, Tensor Cores, PTX, SASS, CUTLASS, CUTE, Hopper/Blackwell runtime profiling.
Explore Program →04
Foundations of Deep Learning
Neural internals, NLP, CNNs, generative models, computation visibility, and architecture trade-offs.
Explore Program →05
Introduction to ML, DL & NLP
ML engineering, probability, Markov models, sequence models, deep learning internals, and Transformers.
Explore Program →Strategic Deep Tech Funding
We don't just fund deep tech.
We build it. We take it to market.
We selectively back deep tech companies where our engineering and go to market expertise can materially change the outcome.
Fund
Strategic capital for technically ambitious companies.
Co-Develop
Our engineers work alongside your team on critical systems.
GTM
We help take your deep tech product to market and drive adoption.