Tokenokio for enterprise
Every AI dollar, attributed to the team that spent it
AI spend arrived without a cost centre. It is spread across subscriptions, API keys, coding agents and seat licences, and finance is asking which team is responsible for which share. Tokenokio produces that answer from real usage, then cuts the bill without downgrading the work.
What this fixes
- Spend is visible in total, invisible by owner
- Attribution by project, team, model, provider and machine — exported as a finance-ready showback CSV, a GL journal, and an R&D-eligible versus capitalise/expense split.
- Frontier models doing work a cheap model handles identically
- The router classifies each job and picks the cheapest model that clears the quality bar for it, escalating only when an answer is weak. The waste index prices this leak before you change anything.
- Seat licences nobody uses
- Import your seat export and idle seats are priced in dollars per month, so the renewal conversation starts from evidence.
- Agents that loop and bill thousands overnight
- Agent Guardian watches burn rate against your own baseline and can kill the process before the loop compounds. A daily cap estimates each call before it runs and blocks anything that would breach it.
- Budget overruns discovered after the month closes
- Month-end spend is projected from real usage and flags amber at 80% and red when you are on course to breach, with the overage in dollars.
Before you ask procurement
Security posture
Usage data is parsed in your browser and stored on your device. We never receive your prompts, outputs, API keys or payment details, and there is no user database to breach. Independent certifications (SOC 2 Type II, ISO/IEC 27001) are on the roadmap and not yet held; what exists today is an adversarial security review with findings remediated and security behaviour enforced by automated tests on every change.
Zero float
We never resell tokens, hold provider credits, or handle your provider payments. Providers bill you directly, so our only incentive is reducing your spend.
Numbers you can defend
Provider-reported and estimated figures are labelled separately and never mixed. Savings claims are capped and conservative, with the assumptions visible and enforced by unit tests.
Questions we get asked
How is this different from our cloud cost tool?
Cloud cost tools see the invoice; they cannot see which prompt, project or agent generated it, or whether a cheaper model would have produced the same result. Tokenokio works from the usage records themselves, so it can attribute spend and change the routing decision that caused it.
Can we roll this out without a security review blocking us?
The architecture is designed to shorten that review: usage data stays on the employee's device, we receive no prompts, outputs, keys or payment details, and there is no user database. SOC 2 Type II and ISO 27001 are on the roadmap and not yet held — we state that plainly rather than implying coverage we do not have.
Does it work with our existing subscriptions and keys?
Yes, that is the design. It runs on the Claude, ChatGPT and API accounts you already hold, and heavy work can run on those subscriptions at no extra marginal cost. There is no new AI vendor to procure.
How do we get team-wide rather than per-person visibility?
Connect a provider admin or reporting key and that account's usage is pulled centrally for every device on it. Per-machine local scans can be exported and merged into the same ledger for anything the admin API does not cover.
Scan one machine free in 60 seconds, then decide if the org-wide view is worth it.