LLM platform selection, prompt architecture, model routing, and fine-tuning/specialization when smaller models win on cost and latency.
We help you design a GenAI program that flexes with model releases, not one that is rebuilt every quarter. That includes how you cache, batch, and route across frontier and open-weight models.
Outcomes
- Coherent reference architecture across your GenAI use cases
- Policies for PII, IP, and acceptable use in prompts and outputs
- Evaluation sets grounded in your content and user journeys
Typical deliverables
- Model comparison and token economics
- Safety filters and content policies
- Runbooks for incident response and model updates
Ready to talk specifics for your organization?
Contact SITS