Maximand is an advisory for financial institutions and other heavy users of AI. We go through your generative-AI usage, find the spending that returns nothing, and take it out.
Spending is climbing fast, and falling per-token prices do little against the rising volume. When the bill finally draws attention, the reflex is to ration the models and pull back seats. That usually backfires. In most estates a large share of the spend produces nothing measurable, and rationing capacity does nothing about it. Find that spend and take it out, and the bill falls without anyone losing a tool they rely on.
A quant fund running $24.0M a year through LLMs scored 26 out of 100, the Tokenmaxxer band. In just over a week we took total AI spend down to $15.3M, held every quality floor, and lifted the share of spend tied to research that reached a live signal from 22% to 80%, so effective research per dollar more than doubled.
The method is the same for any large AI estate. We started with regulated finance because it carries the hardest verification, security, and model-risk requirements, and clearing that bar makes the rest straightforward.
Cost discipline under model-risk and data-residency rules, measured per task or per case, with risk and compliance signing off the quality floor.
Where the gain shows up as research capacity: a lower cost per validated signal and more hypotheses tested per dollar, with research quality held.
Software, support, and operations estates running large LLM workloads, measured on unit cost per resolved task.
A 2 to 4 week diagnostic, measured on your own invoices and observability rather than a questionnaire. You get a Token Efficiency Score, a defensible savings number, and a costed roadmap.
We remediate in priority order and verify every saving on your own ledger, holding a quality floor your risk function signs off. We are paid a share of what is verified.
A standing operating model with an owner, a cadence, and anomaly detection, so the savings hold and the next runaway agent is caught within hours instead of months.
Savings are verified on your own invoices and signed off by your finance team before any fee is charged.
We use vendor-neutral techniques anyone can read about, and we publish ours in full as the Token-Efficiency Standard. What you engage us for is running them inside your model-risk, security, and procurement limits, and proving every figure in a form your finance and risk teams will sign. More on how we work and what we hold to.
Run the free Token Audit scorecard and get your Token Efficiency Score and an indicative savings range in about five minutes.