Right model for the job
Not the largest model by default - the one that meets the bar affordably.
AI · AI & ML Models
Production-grade model selection and ops.
Choose, adapt, and operate models that fit your accuracy, cost, and privacy needs - from foundation models to fine-tunes and classical ML.
Outcomes
What success looks like.
Controlled change
Upgrades that don't surprise users or break prompts.
Clear ownership
Who approves model changes and how rollbacks work.
Capabilities
How we deliver in this area.
Model evaluation
Side-by-side tests on your real tasks and data.
Fine-tuning & adapters
When customization beats prompt-only approaches.
Routing & fallbacks
Cheap models for easy tasks; stronger ones when needed.
Safety filters
Policy checks appropriate to your product.
Lifecycle management
Versions, deprecations, and migration plans.
Cost/performance tradeoffs
Latency and spend dialed to your SLA.
How we run it
A clear path from brief to release.
Seniors stay close to the work. Status stays honest. The process bends to your stage.
01
Task suite
Define the examples that represent production.
02
Bake-off
Compare candidates with fixed evals.
03
Integrate
Wire the winner into apps with logging and limits.
04
Operate
Watch quality and re-run bake-offs when vendors change.
Questions
Straight answers before you commit.
Should we fine-tune or use RAG?
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Should we fine-tune or use RAG?
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Often RAG first for knowledge; fine-tune when style or task format is stable and volume justifies it. We'll recommend with numbers, not slogans.
Can we self-host models?
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Can we self-host models?
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Yes when privacy or cost demands it. Hosting adds ops load - we make that tradeoff explicit.