Clean. Fine-tune.
Never forget.
AI-powered dataset cleaning. Self-serve fine-tuning for Mistral, Llama, Saul, Qwen, Gemma. A continual-learning engine for production fine-tuning — ship a model that keeps learning.
AI-Powered Dataset Cleaning
60+ validators, AI-judge with score-floor-gated rewrite, structural pair audit + judge-based polarity sample for DPO/SimPO, tool-call validation, regex-based PII heuristics.
Open the Optimizer →Self-Serve Fine-tuning
LoRA + QLoRA on six leading 7-9B models. Flat $3.99 per million tokens. No infrastructure to manage. OpenAI-compatible endpoint when training finishes.
See models & pricing →DPO & SimPO Preference Tuning
Hosted Direct Preference Optimization and SimPO on the same open-source LLMs. Reference-free SimPO — no reward model, no PPO, no RLHF stack. Same flat $3.99/M tokens.
See DPO & SimPO →Continual-Learning Engine
Stack medical → legal → finance → code onto the same model. Patent-pending CRMA. −0.17% drift on Mistral-7B (3 seeds) versus +43% baseline.
Read the research →Built for regulated industries
Production-grade security across the API, storage, and runtime. Designed for healthcare, financial, and defense teams handling sensitive data.
- AES-256 encryption
- RBAC
- Audit logging
- HSTS / strict CSP
- GDPR data rights
- Stripe payments
Runs entirely inside your network
Same API, same results, zero data leaves your environment. Ships as a single Docker container — air-gapped, on-prem, or in your private cloud.
- No telemetry
- BYO GPU
- Single binary