Using Jev to Reduce Our Reasoner Cost by 30%
Using Jev Inside Altis, The Insurance Claims Agent
At Altis, we're building an insurance claims agent for property and casualty service providers such as auto repair shops, roofing companies, home restoration vendors. A core part of the product is deep research across thousands of pages of policy documents, OEM repair procedures, and insurer guidelines to produce a well-substantiated claim.
We used to walk these documents with an army of parallel agents on frontier models to maximize recall. But this was prohibitively expensive, roughly ~1M input and ~100K output tokens per run, with no smart way to filter documents before processing. Until Jev.
Our reasoner searches a large technical corpus before making recommendations. The corpus holds manufacturer procedures, estimating guidance, and reference material; the input describes the vehicle, visible damage, estimate lines, and repair context. Most documents have nothing to do with a given repair, yet the original pipeline read all...
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