Foundation models are trained on the internet. Your business runs on proprietary data, internal terminology, and edge cases no general-purpose model has seen. We build the model that closes that gap.

80%prompt token reduction achieved by Indeed through domain-specific fine-tuning

20xvolume scaling after replacing generic prompts with a fine-tuned model

90%of notable AI models in 2024 came from industry, not academia

The decision between a general-purpose model and a custom model comes down to one question: does the task require knowledge that only exists in your data?

Classification against your internal taxonomy, extraction from your specific document formats, recommendation based on your customer behavior patterns, prediction from your operational data. These are problems where a fine-tuned model consistently outperforms a prompted foundation model. Often at a fraction of the inference cost. A classification model trained on your internal categories outperforms a prompted GPT-4 at one-tenth the price per call. That math changes the build-vs-buy calculus fast.

We select architecture based on the problem and the production constraints: latency budget, compute environment, cost per inference. Not based on what is fashionable. Every model ships with holdout test sets that reflect production conditions, performance metrics aligned with the business outcome, and baseline comparisons against both the manual process and off-the-shelf alternatives. If the custom model does not materially outperform the simpler option, we say so. We would rather save you the maintenance burden than sell you complexity you do not need.

Data audit to production model in four to eight weeks. Every deployed model includes drift detection and performance monitoring with automated alerts when the data distribution shifts. The model stays accurate because the infrastructure will not let it degrade silently.