A Model That Decides
Like Your Business
A rented frontier model already made up its mind. It flattens the values your operation actually runs on. We fine-tune small models on your data, your terminology, and your judgment, then deploy them where your compliance team needs them, so the system reasons the way your best operators do.
Prompting has a ceiling. Tuning goes through it.
We ran a study where we gave one frontier model six companies to be, each with opposite values, and asked it to decide like each one. It came through clearly for the safe, agreeable identities and flattened the aggressive, cost-first, hard-negotiating ones toward a coin toss. No matter what we loaded, the model reasoned in its own built-in "security" default at a constant, heavy rate.
That is fine for a chatbot talking to strangers. It is a problem when you need an agent to hold your company's judgment across a thousand decisions. The fix is not a longer prompt. It is a model tuned to your values and running where you control it.
See the charts and the method →Bake in your values
A prompt is a sticky note the model can ignore. Tuning writes your priorities, risk posture, and decision style into the weights, so the model actually holds them across thousands of calls instead of drifting back to its own defaults.
Prompting fixes roughly 70% of behavior problems, then hits a ceiling no instruction reliably holds. Fine-tuning is what gets you past it.
Bake in your knowledge
Your products, your part numbers, your supplier history, the way your best operators actually decide. Tuning teaches the model your world so it reasons in your language, not the internet's average opinion about your industry. We pair it with retrieval for the facts that change daily.
Fine-tuning changes how a model decides; retrieval changes what it can look up. Your proprietary data is the one moat a competitor cannot buy.
Tune to the use case
A small model trained on one job beats a general-purpose giant at that job. We tune per role and per task, so the output is sharper, more consistent, and shaped for how your team actually uses it.
Attorneys preferred a custom legal model over GPT-4 on 97% of reviews. Tuned 7B models beat GPT-4 on 85% of tasks in Predibase's LoRA Land benchmark.
Run it for far less
A tuned small model runs on a single GPU and answers in milliseconds. When an agent makes thousands of decisions a day, the gap between a frontier API and your own tuned model is the difference between a pilot and a system you can afford to scale.
One case study cut cost from ~$12,000 to under $800 a month (5x) and latency from 15 seconds to 0.15 (30x) by moving off a frontier API to a tuned open model.
Stop paying for context
Every instruction and example you repeat in a prompt is tokens you buy on every single call, and one more place the model can misread. Tuning moves that behavior into the weights, so prompts get short and reliable instead of long and fragile.
Baking behavior into the model cuts token use 50 to 75% for steady tasks, and research methods have internalized a repeated prompt to use up to 12x fewer input tokens.
Own it, end to end
Regulated and industrial operations cannot ship formulas, BOMs, and cost structures through a third-party API. We deploy your tuned model on-prem, in your private cloud, or air-gapped, so the training data and every inference stay inside your walls.
On-prem and VPC deployment keeps data under your control by design. NIS2 now names manufacturers and logistics operators directly, on top of GDPR, HIPAA, and SOC 2.
Small, Tuned, and Beating the Giants on the Job That Matters
These are published case studies and benchmarks from across the industry, not our own client numbers. They point the same direction we do.
97%
of reviews preferred a custom legal model over GPT-4
Harvey / OpenAI
85%
of tasks where tuned 7B models beat GPT-4
Predibase LoRA Land
5x / 30x
cheaper to run, faster to respond, vs a frontier API
Checkr case study
90%
accuracy from a tuned small model on the hardest cases
Checkr case study
50–75%
fewer tokens once behavior lives in the weights
Token-optimization research
96% vs 80%
a tuned Phi-3-mini over GPT-4o on a classification task
Published benchmark
Fine-tuning is the behavior, consistency, and efficiency layer. For facts that change daily we pair it with retrieval. We position the two as complementary, never one as a replacement for the other.
Engineering, Not Jailbreaking
Curate the data
We turn your real decisions, records, and operator judgment into a clean, labeled training set. The quality of this step decides the quality of the model, so we do it with your team, not around them.
Tune the behavior
We fine-tune on your task and your values, and we calibrate the decision boundary so the model stops refusing ordinary negotiation, cost-cutting, or competitive-strategy work it currently misreads as risky, while real safety limits stay intact.
Prove it with evals
Nothing ships without an eval suite: accuracy benchmarks against your data, regression tests, and failure-mode coverage. You see the numbers before it goes near production.
Deploy where you need it
On-prem, private cloud, hybrid, or air-gapped. We stand up the serving stack, monitoring, and rollback, then retune as your operation and your data evolve.
Not one model. A team of tuned ones.
No single model can be careful, aggressive, and frugal all at once. A real operation needs all three. So the future is not one giant model, or even one model per company. It is a team of small models, each tuned to the values a role actually needs, working together.
Your procurement agent negotiates hard. Your safety and quality agents stay careful. Your cost-control agent holds the line on margin. Each one keeps its own values instead of collapsing into the same house style. That team is what we build.