AIForFinOps answers the three questions every leader is now asking about AI — with measured evidence, not vibes. We score the quality, value, and spend of your AI, then help you route on the proof. Your prompts and content never leave your environment.
Scores-only & privacy-preserving · works with your existing logs · no rip-and-replace
Groundedness, answer quality, governance & eval coverage across your spend.
Cost per successful outcome vs. the manual alternative — realized value, not promised.
Waste, over-provisioned models, and cache-recoverable spend — surfaced and quantified.
A privacy-preserving loop. We read your usage from the logs you already write, measure a representative sample for quality, and turn the result into a proof you can act on.
We ingest usage — model, tokens, latency, cost — from the logs you already produce. No content leaves your environment.
We qualitatively grade a representative sample of your own traffic — cheaper vs. costlier models, head-to-head — for groundedness and answer quality. That sample is your eval coverage.
Each request type routes to the cheapest model proven good enough. Optional — a drop-in proxy that changes one line of config.
Built-in safety checks make it safe to run unattended — it can never ship, or keep serving, a setup that scores worse.
Route easy traffic to cheaper models — proven adequate on your own data — while hard reasoning stays on the strong model.
Measuring a representative sample instead of every call keeps the quality proof cheap — the same routing decision at a fraction of the cost.
Only scores and usage ever leave the boundary. Prompts and responses stay with you — safe for regulated workloads.
Representative results from real deployments. Customer names and data are withheld — every figure below is illustrative and anonymized.
Usage was fully measured but quality was unproven — Trust Gap 100%. Qualitatively grading a representative sample of traffic for groundedness and answer quality closed the gap and lifted the Trust Index to 90/100.
Cache-recoverable spend and an over-provisioned model tier were quantified from existing logs — no integration — turning a flat bill into a prioritized savings plan.
A quality-measured routing config sent proven-easy segments to a cheaper model at matched answer quality — with continuous monitoring that auto-reverts any segment that starts to slip.
To discuss adopting AIForFinOps in your setup — a measurement pilot from your logs, or the full measure-and-route loop — reach out. We’ll scope it to your stack.
✉ sandhya.natarajan@aiforfinops.tech