Models tuned to your domain.
Fine-tuning, LoRA adapters, distillation and continued pre-training — to get frontier-grade quality on your tasks at production cost.
Frontier quality. Production cost.
A well-tuned smaller model often beats a frontier model on your specific task, at 10x lower cost and 5x lower latency. We engineer that tuning end-to-end.
Six capabilities. One operating system.
Supervised Fine-Tuning
Curated, labeled, deduplicated datasets and tuning runs with bias and toxicity controls.
Adapter Training
LoRA / QLoRA adapters for multi-task serving on a single base model.
Distillation
Compress frontier model behavior into smaller, faster, cheaper student models.
Continued Pre-Training
Domain corpus adaptation when terminology and style matter at a deep level.
Preference Tuning
RLHF / DPO / KTO alignment to your quality and safety criteria.
Serving Stack
vLLM / TensorRT-LLM / SGLang deployment with batching, streaming and routing.
From kickoff to compounding outcomes.
Benchmark
Define tasks, build eval suite, baseline frontier and open models.
Curate
Build training and preference datasets with quality controls.
Train
Fine-tune, adapter or distill — with bias and safety evaluation.
Serve
Production deployment with batching, autoscaling and routing.
Maintain
Ongoing eval, refresh, and re-tuning as data and tasks evolve.
Where this earns its keep.
High-volume classification
Routing, intent, sentiment, PII — at sub-50ms latency and a fraction of frontier cost.
Domain-specific generation
Clinical notes, legal drafting, financial summaries — with terminology and style adherence.
Tool-use and structured output
Smaller fine-tuned models often outperform frontier on reliable JSON and tool calling.
Edge and on-device
Distilled models for offline, edge, and latency-critical deployments.
Frequently asked
Benchmark your custom-LLM economics.
A 60-minute session to identify candidate tasks, project savings, and shape a tuning roadmap.