Model strategy
Choose, train, or distill the model that fits each task instead of sending every request to the largest option.
AI Cost & Performance
More intelligence. Less drag.
AI native engineering partner
We tune the model, context, routing, serving, evaluation, and fallback behavior together to improve quality, speed, reliability, and cost per successful task.
Review an AI workload01Quality ↑02Latency ↓03Cost / task ↓04Reliability ↑
The optimization target / 01
The cheapest model does not always produce the least expensive result.
We measure the full cost of a usable result, including quality, response time, throughput, reliability, maintenance, and the work required to recover from failures.
Production evidenceClear choicesSustainable operations
Optimization levers / 02
Improve the whole system, not only the model.
AI becomes more efficient when the model, context, architecture, and serving approach are designed together.
Context and retrieval
Improve what the model sees through better retrieval, smaller context, structured inputs, and clearer task breakdowns.
Routing and fallback
Send requests to the path that fits them, use confidence and policy signals, and reserve expensive models for work that truly needs them.
Serving architecture
Tune batching, caching, concurrency, streaming, queues, and capacity for the actual traffic and response time the work requires.
Evaluation and operations
Measure cost per successful task, catch regressions, and show operators how quality, speed, and reliability affect one another.
From client work / 03
A smaller model can be the better production choice.
For a production search workload, Claude Sonnet was taking 4 to 5 seconds to respond. We trained a smaller model that produced comparable relevance on the client’s evaluation set, reduced latency to about 500 milliseconds, and saved approximately $250,000 a year in infrastructure costs. The result applies to that workload, not to every task.
System / productionPath controlled
- 01
Define the task
Define the domain, inputs, expected output, and a test set that reflects real use.
- 02
Establish the bar
Measure current relevance, latency, failure patterns, and cost per successful request.
- 03
Specialize
Train the smaller model for the search task and design the serving path around it.
- 04
Route safely
Use confidence thresholds and fallback paths for requests the smaller model should not handle.
Choose a model based on measured behavior for the task, not its brand name.
Interested in our AI services?
Tell us what you are trying to solve.
Share what is stuck, what you have already tried, and what needs to happen next. We will respond with a practical point of view.
- Senior team from the first conversation
- Confidential by default
- No automated sales sequence