Our second pillar: small models, tuned to beat giants on your job

The frontier-model reflex is expensive and usually unnecessary. For a narrow, well-scoped task, a small model that has been properly fine-tuned can match or beat a model thirty times its size — while running local, offline, low-latency, and cheap, on hardware as modest as a six-gigabyte laptop GPU. You own the weights. You own the data. Your proprietary information never leaves your hardware. This is not theory for us. We took a 4B classifier in-house and fine-tuned it from 75% to 95% accuracy on its task in a single short run. That is the multiplier: same job, a fraction of the cost and latency, and a model you control instead of rent.

This is one piece of a larger framework we built and operate in production. The full picture — and how it applies to your business — is in the playbook.

We specialize in healthcare because it is the hardest vertical — strict HIPAA regulation, PHI handling, BAA chains, and zero tolerance for failure. If we can build it for healthcare, we can build it for any industry. We work across verticals.