# AI/ML & Agentic Infrastructure

Production AI systems and the infrastructure agents need inside an engineering organization, from orchestration and skill catalogs to evaluation and monitoring.

## 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.

[Phases and pricing →](https://omnith.com/how-we-work.md#pricing)

## Interested in AI/ML & Agentic Infrastructure?

- **Explore problem and costs**: Questions to understand your constraints.
- **Review past attempts**: What you've tried and where bottlenecks persist.
- **Evaluate fit**: Whether your problem fits what Omnith does.

If we're not a fit, we may be able to refer you to a partner.

Book: https://calendar.app.google/xfbcWJAJhKJaFLy79

## Links

- Book a 30-minute working session: https://calendar.app.google/xfbcWJAJhKJaFLy79
- Email: info@omnith.com
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