Train, Deploy, and Govern your own Small Language Models.

An SLM is a language model in the 100M–10B parameter range, fine-tuned for a specific domain or task. Unlike general-purpose LLMs designed for broad capability, an enterprise SLM is optimized for a narrow, well-defined workflow - fraud classification, clinical note summarization, contract extraction — where the output space is bounded and measurable. A well-tuned 3B parameter SLM consistently outperforms a 70B general LLM zero-shot on these task types, at lower latency and fixed infrastructure cost.

It is a structured delivery engagement — not a hosted platform, not a SaaS fine-tuning tool. At the end you have: a production-deployed SLM running in your infrastructure, a custom evaluation harness tied to your acceptance criteria, operational runbooks your team can execute independently, and a governance documentation package aligned to NIST AI RMF. The model artifacts are yours permanently. No ongoing license is required to run it.

No. Training runs inside your infrastructure. Inference runs inside your infrastructure. The delivery team works within your environment under your access controls. Every data access decision is logged and included in the governance package at engagement close. The architecture supports on-premise, private cloud, and air-gapped environments.

NIST AI RMF organizes AI risk management into four functions: Govern, Map, Measure, and Manage. This engagement is structured so governance activities are lifecycle stage gates, not a post-deployment checklist. Data readiness and risk framing occur in weeks one through three. Evaluation harness engineering occurs before any deployment decision. Monitoring, audit logging, and incident response procedures are part of the deployment package. The governance documentation deliverable is produced as a standard output.

Fine-tuning platforms cover the compute layer. They do not cover data readiness, evaluation harness engineering, governance documentation, or post-deployment operations. Data readiness alone takes four to eight FTE-weeks done correctly. Evaluation harness engineering requires building a custom test suite against your specific acceptance criteria. Governance documentation requires producing the model cards and audit trails your CISO can review. None of that is a platform feature. All of it is covered in this engagement.

Access to your data environment, a technical point of contact on your ML or platform team, and participation in discovery and acceptance criteria definition in week one. For Managed Build engagements, most clients estimate three to five hours per week of internal involvement — primarily in discovery, evaluation criteria review, and handoff.

No ongoing contract required. Your team has a production model, an evaluation harness, operational runbooks, and governance documentation - everything needed to operate independently. The model runs on your infrastructure with no dependency on this engagement continuing. Optional managed operations support - monitoring, retraining cadence, incident response - is available, but the model operates without it.