AI/ML & Agentic Infrastructure

Overview

Omnith takes AI work from proof of concept to production systems that are observable, evaluated, and cost-controlled. That includes the infrastructure agents need to work safely inside an engineering organization: workflow orchestration, shared skill catalogs, and the controls around them.

We focus on applied AI: the right model, data pipeline, and evaluation framework for a concrete business problem. Our experience spans generative AI, classical machine learning, computer vision, and automatic speech recognition, with production work in telecom, defense, and retail.

What We Do

• Agent orchestration and skill marketplaces: Workflow orchestrators and internal skill catalogs that let teams share and govern agent capabilities, with usage tracked for cost and audit.
• Agentic AI and workflow automation: Multi-step LLM agent architectures using tool calling, retrieval-augmented generation (RAG), and structured output that replace manual knowledge-work processes.
• Computer vision: Object detection, segmentation, and classification pipelines for quality inspection, document processing, and real-time video analytics, including multi-camera tracking.
• Automatic speech recognition (ASR): Whisper-based and custom ASR models for transcription, captioning, and voice-driven interfaces, with speaker diarization and language adaptation.
• LLM fine-tuning and evaluation: Domain-specific fine-tunes on open-weight models (Llama, Mistral) with eval harnesses that measure accuracy, latency, and cost before and after deployment.
• ML infrastructure: Feature stores, experiment tracking (MLflow, W&B), model registries, and serving infrastructure (vLLM, Triton, SageMaker) built for reproducibility and fast iteration.
• AI research and prototyping: Prototypes of emerging techniques, such as diffusion models and multi-modal reasoning, tested against your data and your success metrics.

Our Approach

We run AI projects like any other engineering effort: version-controlled code, automated tests, reproducible builds, and success criteria agreed before the first experiment. Models ship with an evaluation report, a monitoring dashboard, and a data-drift detection pipeline so you see performance degrade before your customers do.

We are model-agnostic and vendor-neutral. If a rules engine outperforms an LLM for your use case, we will tell you and build the simpler solution.

Technologies

Python, PyTorch, Hugging Face Transformers, LangChain, LlamaIndex, OpenAI API, Anthropic API, Claude Code, Kiro, OpenCode, Crush, Pi, vLLM, Triton Inference Server, ONNX Runtime, MLflow, Weights & Biases, SageMaker, Vertex AI, CUDA, OpenCV, Whisper, Ray, Argo Workflows, Databricks.