Private AI, Predictable Costs

The company used premium hosted models for routine and complex tasks alike, so inference charges rose with adoption. Teams also wanted tighter control over where sensitive company knowledge was processed.

• Simple summaries used the same costly models as complex work.
• Usage-based inference charges grew as adoption expanded.
• Sensitive prompts needed stronger processing controls.
• Teams wanted AI access without routing every task externally.

Existing model prototypes, reviewed examples, and domain knowledge could support a compact model for frequent tasks. Retrieval and confidence-based routing could reserve higher-capability hosted models for requests that need them.

• Adapt an open-weight model for well-defined routine requests.
• Ground answers in approved internal knowledge.
• Keep routine inference on company-managed infrastructure.
• Escalate complex or uncertain requests to a stronger model.

We adapted and quantized a compact model for local inference, then paired it with retrieval and a lightweight router. Evaluation and cost monitoring guided rollout and capacity planning.

• Select high-volume tasks suited to local model execution.
• Use reviewed examples and approved context to improve responses.
• Evaluate answer quality, latency, and escalation behaviour.
• Track usage and infrastructure costs after deployment.