Infernic

Infernic is an AI engineering company that builds, trains, and maintains models, agents, and data pipelines for production use. It positions itself as an end-to-end provider covering the full path from raw data to deployed inference under a single accountable team. The company works with frontier hardware such as H200, GB200, and Blackwell accelerators to handle deployment, monitoring, and retraining of models.

The company offers six core disciplines under one team, including training-data preparation, custom training and fine-tuning, custom AI agents, data-processing pipelines, and MLOps and maintenance. They emphasize that their data and model weights always remain 100% owned by the client, avoiding black boxes that customers cannot audit or control. This approach ensures nothing falls between the cracks from messy data to a model serving traffic.

Infernic operates on a four-step measured path: Discover to map data and goals, Build to prepare data and train the system, Evaluate against real benchmarks, and Operate to deploy and monitor the model. They use technologies like FP8/FP4 quantization, Mixture-of-Experts, and speculative decoding to ensure models perform when it matters most. This process is designed to keep inference healthy through observability and drift detection.

The company explicitly states they work with frontier hardware including NVIDIA H200, GB200 NVL72, Blackwell B200, and AMD MI300X accelerators. They also utilize NVLink fabric and InfiniBand for high-speed data movement between GPUs during training runs. This hardware stack supports their multi-GPU training and fine-tuning capabilities required for large-scale model development.

Infernic provides high-throughput data-processing pipelines that turn real-world messy data into clean, structured streams ready for models. They also offer custom AI agents equipped with tools, memory, and guardrails that are wired directly into client systems rather than remaining as isolated demos. These agents are designed for task-specific applications with strict operational requirements.

The company maintains 24/7 production monitoring and on-call upkeep to ensure models stay accurate as the world shifts. They perform versioning, observability, and drift detection to keep inference healthy without relying on unowned black boxes. This continuous maintenance is part of their MLOps & maintenance discipline to guarantee reliability.

Infernic claims to use continuous batching, FlashAttention-3, and paged KV-cache to optimize performance. They also leverage TensorRT-LLM and vLLM for efficient serving, alongside Lang for language model management. These features are part of their under-the-hood engineering to ensure the stack performs reliably under production load.

The company states that their training and fine-tuning ranges from LoRA adapters to full training runs, all tuned to specific data and objectives. They rigorously evaluate these models to ensure they meet client requirements before deployment. This flexibility allows them to adapt to various model sizes and complexity levels.

Infernic positions itself as a single accountable team that owns the entire path from messy data to a model serving traffic. They aim to prevent gaps where responsibilities might otherwise fall through during the development and deployment phases. This end-to-end ownership is a key part of their value proposition for production AI engineering.

To get started, visitors are directed to the 'Scope your project' section on their website to see what they do. The company provides a 'Contact' link in the footer for inquiries, and they encourage users to start a project through their contact form. There is no mention of a specific booking system or ordering process beyond initiating a project discussion.

You can reach Infernic by e-mail at contact@infernic.com.

The official website of Infernic is infernic.com.

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