Model choice is an engineering decision with cost, latency, privacy and quality on the table, and it changes as the market does. We benchmark candidates on your actual task rather than on public leaderboards, build so the model can be swapped without rewriting the feature, and keep data residency requirements in the design rather than in a caveat.
Artificial Intelligence & Generative AI
Large Language Models
Choose, ground and run the model behind the feature.
Overview
What the engagement covers
- Benchmarking candidate models on your task and your data
- Retrieval architecture: chunking, embedding and re-ranking
- Fine-tuning only where prompting demonstrably cannot get there
- An abstraction that makes swapping models a configuration change
- Privacy and residency handled in the architecture
What you leave with
- A model chosen on evidence from your own workload
- Freedom to change provider without a rewrite
- Answers grounded in your content rather than invented
Artificial Intelligence & Generative AI
The rest of this practice
Large Language Models, scoped to your estate
Tell us where you are and what it has to be worth. We will come back with a scope, a sequence and a number.