Operator-personalized agentic adaptation
Adaptation passes targeted to a senior operator's tooling, conventions, and security posture — without sacrificing baseline capability.
SVC // 08
AI Model Fine-Tuning
Domain adaptation of open and frontier models — supervised, preference, LoRA/QLoRA, and continual approaches. We pick the smallest intervention that produces measurable lift on your evals.
▌▌▌ WHAT WE DELIVER ▐▐▐
DELIVERABLES
Fine-tuning, treated like production.
- // DATASET PACKAGING Cleaned, deduped, license-checked, with held-out splits and an explicit content policy.
- // SFT / DPO / RFT RECIPES Reproducible training recipes per technique, with sane defaults and a justification.
- // ADAPTER STRATEGY LoRA, QLoRA, or full fine-tune — chosen for the budget and the deployment surface.
- // EVAL HARNESS Domain benchmarks plus a regression suite to catch silent capability loss.
- // SAFETY PASS Red-team probes for jailbreaks, leakage, and brand-unsafe outputs introduced by adaptation.
- // DEPLOYMENT RUNBOOK Quantization, serving, rollback, and version pinning documented before launch.
▌▌▌ REPRESENTATIVE ENGAGEMENTS ▐▐▐
DOSSIER
Selected work — redacted.
▌▌▌ HOW WE WORK ▐▐▐
PROCESS
Dataset. Adapt. Validate.
- // 01 DATASET Packaging is half the job — clean splits, policy filters, and provenance baked in.
- // 02 ADAPT Pick the smallest intervention that moves the evals. Track training runs like production deploys.
- // 03 VALIDATE Domain benchmarks, regression suite, and a red-team pass before anyone touches the production weights.