AI / ML + GenAI

Overview

Artificial intelligence is no longer a research curiosity — it is a competitive lever. Omnith helps product and engineering teams move from proof-of-concept to production AI systems that are reliable, observable, and cost-effective.

We focus on applied AI: solving concrete business problems with the right model, the right data pipeline, and the right evaluation framework.

Our experience spans generative AI, classical machine learning, computer vision, and automatic speech recognition across industries including media, healthcare, logistics, and financial services.

What We Do

• 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.
• 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 systematic 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) designed for reproducibility and rapid iteration.
• AI research and prototyping: Rapid exploration of emerging techniques — from diffusion models to multi-modal reasoning — validated against your data and your success metrics.

Our Approach

We treat AI projects like any other engineering effort: version-controlled code, automated tests, reproducible builds, and clear success criteria agreed upon before the first experiment runs. Every model ships with an evaluation report, a monitoring dashboard, and a data-drift detection pipeline so you know when performance degrades 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, vLLM, Triton Inference Server, ONNX Runtime, MLflow, Weights & Biases, SageMaker, Vertex AI, CUDA, OpenCV, Whisper, Ray.