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Case Studies
EyesInAI started as a way to see which AI model is best for which job. The natural next step: use that same benchmarking on a real product. We take a client’s actual tasks, test every candidate model against the real data, and find the least expensive model that still answers correctly— task by task. Here’s the first one.
A metered API scales with every token. Bills spiral from structure — no caching, over-routing to frontier models, uncapped pipelines — not from using more AI. Read the mechanism and the fix, then see it applied below.
Read the Token TaxBehind every case study is our admin review console. It benchmarks each candidate model — and the AI agent harness itself — against your real tasks, grades every answer against ground truth, and prices each routing decision two honest ways. You walk away with the least-expensive passing model for each job, not a leaderboard guess.
The least expensive model that still passes each task — measured, not assumed.
Which subscription-billed agent work can move to a lower-cost model before metered billing lands.
Metered-equivalent and true burn — so the savings figure holds up to a finance team.
For an equipment-rental company’s field-service support chatbot, we benchmarked 5 models across 11 chatbot actions and built a routing table: the least expensive model that still gets each task right.
The same playbook we run on the DLR chatbot, each lever grounded in what we’ve measured or are building. Start with the low-effort, structural ones; they stop the bleeding before you touch a model.
Benchmark your real tasks; send each to the lowest tier that still clears the quality bar. On DLR that measured 31% in savings per turn at the same accuracy.
Prompt-cache the static system block; set a per-turn budget backstop and a daily spend cap with alerts. Config flags, shipped this week, no model change.
Move high-volume, low-complexity calls onto owned hardware once the break-even math crosses. DLR runs on API today; this is our next lever.
Re-benchmark, propose, approve, re-tier live — no redeploy. Packaged as a portable “chatbot + measured routing” building block on openclaw.ai.
Want this for your product? Tell us about your use case.