The home of Shown Lab — hosting experimental machine learning prototypes, academic research projects, and specialized AI systems consulting.
Each project runs in an isolated containerized environment on its own dedicated subdomain.
A multi-agent workflow framework designed for asynchronous task execution, tool chaining, and automated evaluation.
Hybrid dense/sparse semantic retrieval pipeline with cross-encoder re-ranking for complex technical documents.
Empirical evaluation suite benchmarking LLM hallucination rates, token economics, and inference latency across providers.
Helping startups, research groups, and established teams design, validate, and ship production-grade AI systems.
End-to-end design and deployment of autonomous reasoning workflows, dynamic tool integration, structured output validation, and state machine persistence.
Transform proprietary knowledge repositories into high-accuracy conversational intelligence. We solve hallucination, chunk boundary errors, and sparse context degradation.
Take concepts from whitepaper or notebook to live, interactive web apps within days. Built cleanly to scale seamlessly on platforms like Render, AWS, or GCP.
Quantize, fine-tune, and benchmark open-source and proprietary models to cut latency, optimize GPU memory footprint, and slash per-token cost by up to 80%.