Fine-tune, distill, and operate LLMs with measurable quality, controlled cost, and enforceable policy.
OpenAI Anthropic Llama Mistral
LoRA QLoRA PEFT
vLLM TensorRT-LLM Triton ONNX
MLflow W&B
Ray Kubernetes
Tooling rail: weak-supervision • dedup • PII scrub • golden sets Edge: Leakage prevention and policy-aligned datasets.
Tooling rail: LoRA/QLoRA • PEFT • KD pipelines Edge: Balanced quality vs. cost/latency targets.
Tooling rail: rerank • hybrid search • routing Edge: Σ-Graphion™ graph-aware context for precision.
Tooling rail: BLEU/BERTScore • task rubrics • red team Edge: EthicΞense™ enforces policy-as-code with attestations.
Tooling rail: vLLM • TensorRT-LLM • A/B • canary Edge: Qμβrix™ throughput/latency SLOs with rollback.
Tooling rail: token budgets • caching • quantization Edge: Neuro-Quantis™ inference FinOps with budgets and caps.
Curation + leakage controls
Sandboxed; auditable prompts/outputs.
SLOs + rollback + budgets.
Bring us your hardest deep-tech problem. We'll bring the IP, the method, and measurable outcomes.