A model that sounds like you — and only you.
For teams that need proprietary tone, domain-specific reasoning, structured output formats, or lower inference costs at scale. We fine-tune, distill, and evaluate — and we tell you honestly when fine-tuning won't solve your problem.
Fine-tuning is a last resort. When it's the right tool, it's transformative.
Most teams that want fine-tuning don't actually need it. They need better prompts, better retrieval, or better evaluation. We tell you that before you spend $50k on training runs. When fine-tuning is the right answer — specific tone at scale, proprietary format, domain knowledge too fresh or too specialized for base models, or cost reduction through distillation — we build the entire pipeline.
Fine-tuning without evaluation is guessing. We design the evaluation harness first: the exact behaviors you want to improve and the behaviors you must not regress. We measure before training, after each run, and in production. Fine-tuning is iterative, not a one-shot process.
We work with OpenAI fine-tuning, Anthropic Model Distillation, Hugging Face, and open-source models (Llama, Mistral, Qwen, Phi). We choose the base model and training approach based on your latency, privacy, and cost requirements — not on what's trending.
What we do.
Supervised fine-tuning (SFT)
Train on high-quality demonstration data to teach specific formats, tones, domain knowledge, or task behaviors the base model doesn't reliably exhibit.
Synthetic data generation
Generate thousands of high-quality training examples using a teacher model — for tasks where real labeled data is scarce, expensive, or confidential.
LoRA / QLoRA parameter-efficient training
Low-rank adaptation for rapid iteration on smaller GPUs. Production-ready adapters that load on top of quantized base models for cost-efficient inference.
Model distillation
Distill expensive frontier model outputs into a smaller, faster, cheaper model you can deploy at scale — trained to match the frontier's output distribution on your specific tasks.
Eval-driven iteration
We build ground-truth eval suites before training, measure regressions after every run, and iterate until the target behaviors are consistent — not just better than baseline.
Private & on-prem deployment
Quantized models served on your AWS, GCP, or Azure infrastructure with vLLM or TensorRT-LLM. Sub-100ms P95 latency for models up to 70B parameters.
How we do it.
Needs audit
We audit whether fine-tuning is actually the right solution. We benchmark the base model with optimized prompting and RAG first. If fine-tuning is warranted, we define the exact behaviors to improve.
Eval design
We design the evaluation harness before touching training data — target behaviors, regression behaviors, and the metrics we'll track across every training run.
Data pipeline
We source, clean, and curate training data. If real data is scarce, we generate synthetic examples using a frontier teacher model and validate them against your eval set.
Training & eval
We run training on your chosen base model, measure against the eval harness after every run, and iterate until target behaviors are reliable without regressing others.
Deployment
We deploy the model on your infrastructure with a serving layer (vLLM, TensorRT-LLM, or Ollama), latency benchmarks, and a cost-per-token dashboard.
Tools we use.
Frequently asked.
5 questions answered. Still have one? Reach out.
For supervised fine-tuning, as few as 50–200 high-quality examples can meaningfully shift a model's behavior on a narrow task. For broader capability changes, 1,000–10,000+ examples. We help you determine the right target and can generate synthetic data to fill gaps.