OWN YOUR MODELS
Your models, your data, your economics.
Inference bills grow with every workflow. Data rules keep tightening. And open-source models keep closing the gap for more of your work. Moving to models you own, open-source, smaller, or custom-trained, cuts cost and keeps your data in your own stack. The hard part is switching without quality quietly collapsing. That is an evals problem, and evals are our specialty.
THE TREND
Good enough is arriving fast.
For a growing share of production tasks, a smaller or open-source model clears the bar, at a fraction of the per-task cost and running inside your own infrastructure, where the data never leaves. Ownership also means independence: your economics stop tracking one vendor's price list, and your roadmap stops waiting on theirs. The gap is measurable: the best open-weight models now trail the closed frontier by only a few months, and inference prices at constant capability have fallen roughly tenfold per year.
THE CATCH
A swap without proof is an outage on a delay.
Model migrations done on vibes fail quietly: the demo looks fine, and the regressions surface weeks later, in production, in front of customers. The only safe way to change models is to know exactly what your system must do and to test every candidate against it.
HOW IT WORKS
Prove it, then switch.
We define what working means for your system and codify it into evals: quality, speed, and cost per task, in one standard. Then every candidate model, open source, smaller, or custom, gets benchmarked against it. What passes migrates. What matters most stays on the stronger model. Monitoring watches for drift after the switch, and every future swap re-proves itself against the same standard. The standard is how you buy cost down without buying risk.
Where prompting tops out, we train the model: purpose-built for your domain, running in your stack, owned by you.
COMMON QUESTIONS
Asked by model owners, answered plainly.
Are open-source models actually good enough for production?
For a growing share of tasks, yes: the best open-weight models now trail the closed frontier by only a few months. The honest answer for your system comes from testing candidates against your own standard, which is exactly what the diagnostic does. Benchmarks are somebody else's tasks; your evals are yours.
If open models are so good, why do most enterprises still default to closed ones?
Confidence. Menlo Ventures measured open-source share of enterprise LLM workloads falling from 19 to 13 percent in early 2025 even as open-model quality rose, because swapping models without proof is a risk most teams cannot price. Evals turn that risk into a measurement, and the measurement often says switch.
What about EU rules and data residency?
The EU AI Act's transparency duties took effect in August 2026 even as the high-risk deadlines moved to late 2027, and data-residency expectations keep tightening in regulated industries. Models running inside your own environment turn most of those conversations into short ones.
When should we train our own model instead of prompting a rented one?
When the evals show a ceiling that better prompts and retrieval cannot break. That is a custom-model problem, common in pricing, forecasting, and domain-heavy work, and the same standard that gated the migration gates the training.
LET'S TALK
Bring us the hard problem.
We'll bring the team that ships.