Clarity Conserve is the meter and the retrieval layer for your AI spend. It measures where every token goes — per stage, per request, per day — and it replaces documents re-sent to the model with retrieved, cited passages from your verified knowledge base.
The problem nobody put in the budget
Headcount you approve. Infrastructure you provision. Tokens just accumulate — one request at a time, invisibly, until the invoice arrives.
And most of it isn't work. It's the same context, shipped again and again inside a single logical task, because that was the simplest way to make the system correct.
The waste isn't in the answers. It's in the setup, repeated.
Built-in measurement
So Clarity Conserve ships the meter alongside the engine:
Point it at your workload and reconcile what it reports against your provider's bill, line by line.
Retrieval, not stuffing
When someone attaches a document, the naive approach puts the whole file in the prompt — again on the next message, and the next.
Clarity Conserve doesn't:
This is not prompt caching. Caching re-serves the same oversized prompt at a discount — the document still ships, still occupies the context window, just costs less per trip. Conserve changes what enters the prompt at all: only the passages relevant to the request, drawn from your verified knowledge base, each carrying its provenance. Caching makes the repetition cheaper. Retrieval makes it unnecessary.
So a long attachment contributes a few kilobytes rather than its full length — and the answer can still cite the exact passage it relied on. The same retrieval layer powers Clarity Secure — the provenance that makes an answer enforceable is the provenance that makes it citable here.
The layer that makes an answer citable is the layer that makes it cheap. Same investment, twice the return.
Proof, not projections
The numbers on cost pages are usually estimates. These aren't. They are what Clarity Conserve's meter reported when we instrumented our own eight-stage document-reasoning pipeline — before we optimized anything, because the measurement had to come first.
One run, one article, eight stages: 246,219 tokens. And a surprise: input barely grew from stage 1 to stage 8 — 1.39×, not 8×. The cost wasn't accumulation. It was a fixed floor: every stage paid ~25,000 tokens for the same reference material before doing any work.
of all input: the same envelope, sent eight times.
everything that genuinely accumulated.
~29,500 tokens in 11 tokens out
The clearest single example: one stage spends ~29,500 input tokens to produce 11 output tokens. Its prompt reads three small sections and never once looks at the largest thing it's being sent.
We went in expecting runaway growth. The data said otherwise, and we corrected our own diagnosis in writing. The fix that follows from the corrected measurement is a different fix — and about six times more valuable — than the one we would have built on the assumption.
Eight stages was one pipeline — ours. The floor gets paid again on every run, of every pipeline, at whatever volume you operate. Point the same meter at yours and see what it reports.
What this is worth
The only number that matters is the one derived from your volume and your provider's rates. What we will do is instrument your workload and show you the calculation, line by line, from your own telemetry.