Research first. Then a system you keep.
Every engagement starts with your problem, not our product. We study it, find what works on your real cases, build the smallest thing that solves it, and hand it over.
How we go deep on one problem.
Four steps, in order. Each one ends with something you can see and judge before the next begins.
We study the problem.
We learn your industry, your data and what a good answer looks like, before anything gets built.
You getA plain answer: whether research can fix it, and what it would take.
We find what actually works.
We test approaches on your real cases, not public benchmarks, and keep only what holds up.
You getResults on your own cases, so you can see what works before it's built.
We build the smallest thing that solves it.
The smallest model that does the job well, running where your data already lives. It's faster, and it costs less to run.
You getA working system, built for your problem and tested on your data.
You keep it.
You own the models, the code and the data. No lock-in. And it keeps improving from real work.
You getEverything we built, yours to run, change or take elsewhere.
What you can walk away with.
Most engagements end in one or more of these. Which ones depends on your problem, not on what we sell.
A small model trained on your data
We turn the routine work your systems already do into clean training data, and train a compact model for it. Work that ran on an expensive frontier model runs on a small one you own.
AI that runs where your data lives
Models and agents run inside your own environment, on your servers or your cloud account. Your data, model weights and logs don't leave.
Lower running costs
Small and open models handle the routine calls; bigger models run only for the steps that need them. You pay for the expensive model only where it earns it.
Agents that improve from real work
Agents that learn from their own runs, so they get better at your work over time instead of staying as good as the day they shipped.
Whatever your problem needs
From a research question to a system in production: we scope it, build it and help you run it, so your team doesn't carry the build.
Some problems became products anyone can use. See the products
Questions, answered plainly.
What kind of problems do you take on?
Hard business problems that off-the-shelf AI can't solve: where a general model gives generic answers, costs too much to run, or can't use your data. If you're not sure your problem fits, bring it to us and we'll tell you plainly.
How is this different from going to a big AI lab?
Big labs build for everyone and want you on their platform for good. We don't sell a model, so we have no reason to keep you on one. We build the smallest system that solves your problem and hand it over.
Who owns what you build?
You do: the models, the code and the data. If you stop working with us, you keep everything.
Does our data have to leave our environment?
No. We can build and run the system where your data already lives, including on your own servers.
Why small models?
Most calls an AI system makes are short and routine. A small model trained on your data handles those faster and for less, and bigger models are kept for the steps that need them.
Where do your products fit in?
Each one started as a problem we had to solve. If one fits your problem, we'll use it. If not, we build what your problem needs.
How do we start?
Bring us the problem, or book a 30-minute call. We'll ask about your data and what a good answer looks like, then tell you plainly whether research can fix it and what it would take.
Bring us your hardest problem.
Tell us what isn't working. We'll tell you plainly whether research can fix it, and what it would take.
Or email hello@octaneural.com.