Production-grade HPC and AI/ML solutions optimized for true performance. From model inference optimization and DevOps automation to applied AI development and inference-optimized model creation.
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Innovative tools and platforms designed to solve real-world problems.
Field reports for operators who care about margin and reliability. We map the hidden layers of AI spend and show what to fix first.
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A 4-part breakdown of the cost paradox: 99.7% token price drop, 3× bill growth, and 72% spend hiding outside inference.
Includes benchmark numbers, hidden-cost anatomy, and an operator-ready optimization sequence.
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Failure-tax patterns in agentic systems, with concrete safeguards for context drift, tool-call loops, and noisy retrieval.
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Deep dives into model optimization, HPC, MLOps, DevOps, and production-grade AI/ML engineering.
Every RAG system hides an Embedding + Rerank gateway behind its API. We built the gateway in Python, Rust (ONNX), and a Split architecture, benchmarked on a single GCP node, and compared footprint, throughput, and latency. Rust beats Python by ~28% RPS and 67% less memory — same model, same API.
A step-by-step, accessible guide to compressing Orpheus-3B TTS via self-knowledge distillation using Unsloth, SNAC and LoRA.
Most Python developers think threads can't parallelize CPU work. Wrong. We benchmarked RAG ingestion across Python 3.13, 3.14, and 3.14t: threads are 70% faster than multiprocessing with 75% less memory — because NumPy and PyTorch release the GIL. Your infra bill doesn't need more pods. It needs better package choices.
Applied AI development involves the practical implementation of artificial intelligence technologies to solve real-world business problems. At NavyaAI, we specialize in building production-grade AI systems—including LLMs, agents, and conventional ML models—that deliver measurable ROI and are governed, explainable, and reliable from day one.
Model inference optimization focuses on improving the speed, efficiency, and resource utilization of AI models during deployment. This includes techniques like quantization, pruning, knowledge distillation, and using specialized hardware or inference frameworks. Our optimization services reduce memory footprint, lower computation complexity, and decrease inference latency while maintaining model performance.
AI ML consulting costs vary based on project scope, complexity, and duration. At NavyaAI, we offer flexible engagement models tailored to your needs. We provide transparent pricing and work with businesses of all sizes. Contact us for a customized quote based on your specific requirements.
We work with a wide range of technologies including Rust, Python, Golang, Mojo, and C. Our expertise spans HPC solutions, MLOps, DevOps automation, and production-grade AI/ML systems. We choose the best technology stack based on your performance requirements and infrastructure constraints.
Yes, NavyaAI specializes in end-to-end AI application development. From initial strategy and model design to deployment, optimization, and ongoing maintenance, we handle the complete lifecycle of AI applications. Our services include model inference optimization, DevOps automation, and production-grade system development.
