Long context that stays sharp
A million tokens is only useful if attention holds at the far end, so Axon is trained and evaluated on retrieval deep into the context.
Every answer arrives with the steps, the sources and the things it chose not to do, so you can check it before your customers do.
Opened arr_by_cohort.csv, churn_q3.sql and the Q3 board deck, 41,208 tokens in all. Two of the three defined “active account” differently, so it kept the warehouse definition and logged the conflict.
Did gross churn actually move? Which segment moved it? Does seat count explain that segment? Did enterprise hold flat? Ordered so questions two and three could share a single query.
Joined subscriptions to accounts across six quarters, grouped by cohort and seat band. 812 rows back in 2.1s. The result was cached and reused by the check step instead of being re-queried.
Recomputed 4.1% and 0.6% from raw rows instead of the pivot table. Both matched to two decimals, so nothing needed correcting and both numbers are safe to quote.
Two sentences, six sources. A third claim about expansion revenue was dropped because only the board deck supported it, and the deck is built from the same warehouse data.
Each one is a design decision that changes what you can safely put Axon in front of.
A million tokens is only useful if attention holds at the far end, so Axon is trained and evaluated on retrieval deep into the context.
It writes and runs code, queries your warehouse and reads your docs, then tells you which of those it skipped and why.
Every answer ships with the steps that produced it. Disagree with step three and you can re-run from there.
We test retrieval at 900k tokens of context, because that's where headline numbers stop holding up.
Run by the Axon evaluation team on Axon 2 at general availability, with the same prompts, trial counts and grader for every model.
Axon leaves enough evidence with each answer for the next person to review, challenge and approve it.
A global operations team asks Axon to review policy documents, customer records, and the latest service events before recommending an action. Low-confidence cases stop in a human queue; high-confidence cases ship with the trace attached.
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