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We identify, clean, and structure your proprietary datasets to construct a high-quality training corpus. This process includes deduplication, filtering, and synthetic data generation to ensure optimal model performance.
We evaluate and select the best open-source foundation model based on parameters, licensing, and latency goals. We match your compute budget with the appropriate model size for your task.
We perform supervised fine-tuning or full parameter pre-training depending on the complexity of your requirements. We use parameter-efficient techniques to keep compute costs manageable.
We implement reinforcement learning from human feedback or direct preference optimization to align model behaviors. This stage introduces strict safety guardrails and system prompts.
We test the model against custom validation sets to measure accuracy, hallucinations, and domain knowledge. We benchmark throughput and latency under simulated production loads.
We apply quantization techniques to reduce the hardware footprint and deploy the model using vLLM. The deployment is fully integrated with your secure enterprise infrastructure.
We believe in radical transparency. You'll always know where your project stands and what comes next.
Progress reports every week
Communicate with your team
Clear deliverable checkpoints
Complete technical handoff
Let's begin with a conversation about your project goals.