AI/ML Model Selection and Tuning.
Model selection and tuning, treated as the engineering work it actually is.
What it is.
Model selection and tuning is where most AI/ML projects either earn their cost or quietly fail. We engage on the engineering work that turns a chosen model into a system that meets your accuracy, latency, and cost targets, and stays meeting them as your data changes. We establish the baseline against your domain data and hardware, then work the levers that move the numbers: model choice, fine-tuning approach, hyperparameters, data augmentation, prompt design, retrieval architecture for RAG. The deliverable is a tuned model with documented performance, plus the evaluation harness that lets your team re-tune as data evolves.
How we engage.
Three phases. Each phase ends when its decisions are made and proven.
Current performance measured against your data and hardware. Success defined in measurable terms: accuracy, latency, throughput, cost per inference.
Work the levers (model variants, fine-tuning, hyperparameters, data augmentation, retrieval, prompts), measuring each round against the baseline. The phase resolves when the targets are met or the ceiling is established and understood.
Packaged configuration, reusable evaluation harness, deployment notes, re-tuning runbook.
What you get.
Who this is for.
You have a model in production that's not hitting targets. You're choosing between a fine-tuned smaller model and a prompted frontier model. You're building RAG and retrieval quality is bottlenecking answers. You inherited a model and can't tell if it's good, bad, or just unevaluated.
Why Quest1.
We treat tuning as engineering, not a black-box exercise. Our engineers work with the partner teams behind the platforms (Voyage AI for embeddings, deepset for retrieval, ClickHouse for analytical workloads), and that depth shows up in the models we ship.
Have a model that needs to perform better?
Tell us what it's doing and what it needs to do. We'll come back with a tuning plan.