AI Inference Optimisation Software

An AI inference compiler that compiles PyTorch models to optimized GPU and ASIC backends using techniques such as megakernel fusion, quantization, disaggregated prefill/decode serving, and large-scale kernel search to maximize throughput and minimize latency in production.

An open-source LLM inference and serving engine that maximizes throughput and minimizes cost through PagedAttention, continuous batching, quantization, disaggregated prefill/decode, and distributed inference across diverse hardware, with a drop-in OpenAI-compatible API.

A serverless and on-demand AI inference platform built around a three-layer KV cache architecture that reuses repeated context—prompts, documents, tools, and conversation history—across requests, reducing per-token costs to $0 for cached tokens and improving response latency for agent workflows, RAG apps, and multi-turn conversations.

An edge-based gateway that intercepts requests from coding agents and LLM applications to apply context compression (tool result trimming, tool surface reduction, output brevity), intelligent multi-provider routing with automatic fallback, and session- and team-level token observability—reducing token costs by up to 70% without modifying the underlying model or user-facing application.

An agentic CLI that automates production LLM inference optimization by profiling workloads, selecting the best inference engine (vLLM, SGLang, TensorRT-LLM, etc.), tuning configurations via Bayesian search, synthesizing custom CUDA/ROCm/Triton kernels, and validating deployments with functional, load, and security tests — maximizing GPU throughput and minimizing latency on user-owned hardware.

Coworker AI (coworker.ai) is an enterprise AI agent platform that deploys autonomous agents across a company's tech…