Solution brief
Fine-tuning & RAG
Move from generic models to domain-accurate assistants. OWS combines elastic GPU pools for training jobs with patterns for retrieval, evaluation, and safe rollout.
What we deliver
- Adaptation — Parameter-efficient and full fine-tuning jobs with experiment tracking and reproducible environments.
- RAG stack — Ingestion, chunking, embedding jobs, and integration with managed vector search patterns.
- Data boundaries — Private networking, customer-managed keys, and deployment options that keep data on your side.
- Quality loops — Offline eval harnesses, human-in-the-loop hooks, and drift checks before promotion.
Typical engagement
- 1Discovery — workload profile, SLOs, data residency, and budget.
- 2Architecture — cluster topology, APIs, and integration points.
- 3Pilot — limited production or benchmark phase with clear exit criteria.
- 4Scale — hardening, FinOps, and continuous optimization.
Architecture & security
Designs are adapted per customer: VPC-style isolation, encryption in transit and at rest, secrets management, and least-privilege access to control planes. We document data flows for security review and support private connectivity options where required.
Success metrics
We align on measurable outcomes — training throughput (tokens or samples per dollar), inference p99 latency, cost per 1M tokens, job completion rates, and uptime against agreed SLOs. Dashboards and monthly reviews keep both teams honest.
Related products
This solution composes OWS products. Your team can start from any layer and expand.