6x lower cost
28% more accurate
$6$1
Frontier model
Post-trained open-weight
Hospital claim appealsR1, Oct 2026
Turn agent runs into your own fine-tuned models.
For teams with agents in production. Every correction your experts make becomes training data, and your agents get better with every run, on your own model.
curate
context
run
evaluate
optimise
train
v1
v2
Your own modelv3
Are domain-trained open-weight models any better?
Open models now sit within about 3% of the frontier. Post-trained for one task, they match it or beat it for a fraction of the cost.
5x faster
40x lower cost · Matched accuracy
Frontier model
5×
Post-trained open-weight
1×
Time to finish the same task
Vendor credit reviewScale AI, Oct 2026
Arena gap: Stanford HAI, AI Index 2026.
What’s standing
in your way.
AI Natives cross these to get ahead.
Your agents today
- Messy data
- Can't hire researchers
- Every fix gets lost
- Can't see failures
- Model never learns
- Data can't leave
Your own model
Clear all hurdles with SynOS.
How your agents
get better.
SynOS inside your org
- 01
Works on live data.
Your agent runs against your real systems. Every write is recorded rather than executed.
- 02
Tracks Agent performance.
Every run traced and scored against your evals, with expert corrections captured where they already work.
- 03
Generates training datasets.
Runs and expert corrections become eval and training data, kept next to every run before them.
- 04
Builds your own model.
Prompts are optimised first, then your own open-weight model is post-trained on that data.
SynOS agents do the rest of the lab work: they curate the data, build the context layer, trace every run, write the training sets and build the apps your teams use. A small team of your experts and engineers directs it.
What you get.
SynOS is installed in your cloud, above your existing systems and below your agents.
The Brain.
The context every agent and every training run reads from: your systems, one record per entity, and the procedures your experts follow.
- Nothing migrated.Sources are connected in place.
- One record per entity.Resolved across every system.
- Procedures, written down.Held next to the data they apply to.
The Agent Runtime.
Where agents work on live data. Every write is intercepted and recorded rather than executed, then scored.
- Practice on live data.No copy of the business to keep in sync.
- Corrections where experts work.Each one saved as eval and training data.
- Every run priced.Per model and per provider.
The Training Plane.
Where runs and corrections become evals and training data, and your own model is post-trained on them.
v1shipped
v2ships
v3held
Illustrative. A release that scores worse than the last one does not ship.
- No research team.Platform agents build environments and clean data.
- Gated on your evals.Each release is scored against the last.
- Your own model.Its weights stay in your account.
Building a vertical SaaS product? See SynOS for vertical SaaS ›
Questions.
Do we need an ML or research team?
No. SynOS platform agents build the training environments, curate and clean the data and generate the training sets, which is the work a research team would otherwise do. A small team of domain experts and engineers runs it with support from SynOS.
Where do our data and the model live?
In your own cloud account. SynOS installs there and reads your systems in place. Traces, corrections, training data and model weights all stay inside your boundary.
What happens to the model and the data if we stop?
They stay in your account. The weights, evals, training data and traces are yours whether or not you carry on with SynOS.
How much expert time does it take?
Experts review and correct agent runs in the tools they already use, so the work looks like their normal review of output. How much time it takes depends on the use case and on how often the agent is wrong.
What does a first use case look like?
One workflow your experts already do, with a clear idea of what a correct result is. SynOS connects the systems it touches, agents run on live data with their writes recorded, and your experts' corrections start the eval set and the training data.
How is this different from a hosted fine-tuning API?
A fine-tuning API starts from a dataset you have already built and trains on the provider's infrastructure. SynOS builds the environment and the dataset from your systems and your experts' corrections, trains inside your cloud and gates each release on your evals.
Bring one workflow.
One task your experts already check, with a clear idea of what correct looks like. SynOS connects the systems it touches, and your experts' first corrections start the training data.
Your weights, data and traces stay yours if you stop.