ServiceMODEL BUILDING/ model-building
Fine-tuning & Model Building (from 0)
Zero-to-model: dataset design, labeling, LoRA/full fine-tuning, eval gates, deployment, monitoring, and retraining loops.
Time-to-MVP
2–6 weeks
Integrations
CRM / Ops / API
Quality
Eval + monitoring
Overview
This is for you if…
If you need fine-tuning on private/domain data (LoRA or full).
If you want a true zero-to-model pipeline: collect → clean → label → train.
If you need a reproducible training + eval + release workflow (not a notebook).
Overview
Deliverables
Dataset strategy + labeling guidelines
LoRA / full fine-tuning pipeline + checkpoints
Eval suite + regression gates + drift monitoring
Overview
Outcomes
Higher accuracy
Task-specific tuning + proper eval sets.
Reproducible pipeline
Data → train → eval → release gates.
Production-ready
Monitoring + retrain plan + versioning.
Process
Simple 3 steps
01
Discovery
Goals, data, integrations. Short audit + plan.
02
Build
Iterative delivery: prototype → production. Tests + controls.
03
Operate
Metrics, monitoring, drift. Continuous tuning.
FAQ
Short answers
Do you support private data fine-tuning?+
Yes — we set up secure data handling + reproducible training runs.
LoRA or full fine-tune?+
Depends on constraints — we pick based on quality target, cost, latency, and update frequency.
Can you build a model from scratch?+
If it makes sense (small domain models). Usually we fine-tune strong base models for speed and quality.
data • fine-tune • eval gate • deploy • drift • retrain
Security + quality
Production controls
Logging, alerts, release gates — with documented operation.
Next step
15 minutes — and scope is clear
We’ll send a short checklist, then propose timeline and first metrics.