Long context that stays sharp
A million tokens is only useful if attention holds at the far end. Axon is trained and evaluated on retrieval at depth, not on the first page.
Capabilities
Three things it does differently
Not a longer list of features — three design decisions that change what you can safely put it in front of.
Tools it decides to use
It writes and runs code, queries your warehouse and reads your docs — and tells you which of those it skipped, and why.
A trace you can audit
Every answer ships with the steps that produced it. Disagree with step three and you can re-run from there.
Benchmarks
Measured where it actually breaks
Retrieval at 900k tokens of context — the case where every model's headline number stops being true.
Deep-context retrieval accuracy
Needle-in-haystack at 900k tokens · 2,000 trials per model · higher is better
Illustrative figures for a design template — not a published evaluation. Competitor names are placeholders. Run your own evals before choosing a model.
Runs where your data already lives
- Your VPCSingle-tenant deployment in AWS, GCP or Azure, in the region you name.
- No training on youInputs and outputs are never used for training. Contractual, not a setting.
- Zero retention modePrompts discarded at response time; only a hashed request id survives.
- Audit that exportsEvery trace, tool call and citation available over API for your SIEM.
Ask it something hard.
$5 of credit on signup · No card required