Services · Token Governance

Do you know what your team's AI is costing you: per person, per month?

Almost nobody can answer that. Employees are querying AI tools all day with no visibility, no limits, no caching. They pay for the same question dozens of times. We put a meter on it, find the waste, and take a cut of what we save you. Zero risk on your end.

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What it is

A governance layer between your employees and their AI tools.

Every query is logged, rate-limited, cached, and routed to the cheapest model capable of answering it. You get full visibility and control. You stop paying for the same question 40 times a day.

This is not a technology product. It is a cost-containment engagement. The CFO is the buyer. The trigger is the moment someone gave your team access to an AI tool with no follow-up about budgets or oversight.

Full spend visibility

Every query attributed to a user and department. You see who is spending what, on which tools, and why.

Semantic caching

Queries similar to ones already answered return from cache in milliseconds at zero cost. The most common queries (often 30 to 50% of total volume) stop hitting the API.

Smart model routing

Simple questions go to cheap models. Complex ones go to expensive ones. The routing logic is tuned to your actual query patterns.

Rate limiting and budgets

Per-user and per-department monthly caps. No surprise bills. No individual running up the tab.

Compliance filters

PHI-blocking, PII detection, and custom content filters at the edge, before any query reaches an external model. For regulated clients, this is the compliance product.

Monthly savings report

What the layer saved last month, in dollars. Your cut. Our cut. The running total since the engagement started.

Per-Team Budgets & Quotas

Monthly caps set by department, team, or individual, enforced at the proxy layer before the overage happens, not discovered in the billing statement after.

Model-Routing Policy

Explicit rules for which query types go to which models, tuned to your actual query mix so capability and cost are both optimized, not just one or the other.

Prompt-Caching Strategy

Identify the high-frequency, low-variance queries that are candidates for semantic caching, build the cache layer, and measure the hit rate. The fastest path to cost reduction in most deployments.

Usage Analytics & Chargeback

Per-department spend attribution with enough granularity to support internal chargeback or billable-matter allocation: the audit trail that makes AI spend a managed line item.

Shadow-AI Discovery

Surface the AI tools employees are using outside the governance layer: unauthorized subscriptions, personal API keys, browser extensions, so the spend picture is complete, not just the tools IT approved.

Vendor-Contract Rightsizing

Audit active AI vendor contracts against actual usage patterns and model capabilities, then right-size seats, tiers, and commitments at renewal, cutting spend without cutting capability.

How it works

Audit first. Govern second. Compound from there.

The audit produces the discovery report. The governance layer installs the savings. Ongoing management keeps them compounding. No commitment required until you see the audit.

30-day audit: the discovery report

We instrument your AI traffic for 30 days. The report names spend by user and department, identifies the queries running 40 times a day, surfaces questions going to expensive models that a cheaper one handles just as well, and calculates the cache-hit rate. You see exactly where the money is going before you commit to anything.

Governance layer deployment

We place a governance proxy between your employees and their AI tools. Every query is logged, rate-limited, cached, and routed to the cheapest model capable of answering it. The layer is invisible to users. In some cases it is faster, because repeated queries hit the cache instead of the API.

Ongoing governance: compounding savings

Usage patterns shift, models improve, new tools get added. The governance layer evolves with them. We monitor, tune routing rules, refresh cache logic, and report monthly on what it saved. The savings compound as the layer gets smarter about your operation.

Performance pricing

We only make real money when we save you real money.

Setup is a flat project fee covering the 30-day audit and governance layer deployment. After that, ongoing governance is priced as a percentage of monthly savings. The exact structure is scoped in the diagnostic and put in writing before you commit.

The math is straightforward: semantic caching eliminates a significant share of redundant queries, smart routing moves cheap work to cheap models, and our cut is a fraction of what we save you. If the savings are real, the fee is real. If the meter finds nothing, you have lost almost nothing.

Example above is illustrative. Actual savings depend on query volume, model mix, cache-hit rate, and routing efficiency.

Start with the audit

Performance-based

Priced as a share of measured savings, so the risk sits with us

Near-zero floor

If the meter finds nothing, you have lost almost nothing

Low overhead

Infrastructure cost to run the governance layer is minimal, so savings go to you

Aligned

If we save you nothing meaningful, you owe almost nothing

Who it's for

The CFO is the buyer.

This is cost containment, not technology. Any company with 20 or more employees using AI tools and no controls qualifies. The qualifying question is simple: did someone give your team access to an AI tool without follow-up on budgets or oversight?

The qualifying signal

"We gave everyone access to [an AI tool]." No budget set, no visibility, no idea what it's costing. This is the conversation that starts the engagement.

The compliance angle

For regulated industries (healthcare, legal, financial services) the governance layer also adds PHI-blocking filters and a full audit trail. For those clients, this becomes a compliance product, not just a cost one.

The scale floor

We've deployed this for teams of 20 and organizations of 2,000. The economics work at both ends. The floor is wherever the monthly AI spend is meaningful enough to justify a meter.

The growth argument

If you're not spending much on AI yet, install the meter now. The audit establishes the baseline. When usage grows, and it will, the governance layer is already in place.

In practice

Three scenarios where the meter changes everything.

Dollar outcomes are illustrative where modeled. We label which is which.

40-attorney law firm

Per-attorney monthly budgets enforce spend limits by timekeeper. Case-law lookup queries sent to the API dozens of times a day by different associates are cached on the second hit. Governance typically removes five figures per month from the AI bill. Full audit trail for each attorney's usage, with billable-matter attribution included.

Dollar outcomes illustrative

8-location auto-dealer group

Repair summary generation is typically the highest-volume query across dealer locations. Semantic caching eliminates most of the redundancy. Monthly spend drops significantly once caching is tuned. Service advisors notice queries running faster, not slower.

Dollar outcomes illustrative

Regional hospital network

PHI flows to a cloud model with no audit trail and no blocking filters. The governance proxy adds PHI detection at the edge. Queries containing protected health information are blocked before they reach any external API. The compliance posture moves from exposed to defensible.

A note on scope

Token governance is often a low-risk place to start a relationship. The ROI is fast, the setup is contained, and the audit produces intelligence that informs everything else we build. But it is not a package. Like everything we do, it is scoped to your operation, and it is diagnostic-first. We start with the audit because the audit tells us what governance actually looks like for your team, not a generic template.

Questions

Straight answers.

Will this slow our people down?

No, and for repeated queries, it makes things faster. Semantic caching returns the answer in milliseconds when a query is similar to one already answered. Routing is invisible; employees use the same interfaces they always have. The governance layer is behind the scenes.

What if we barely spend anything on AI yet?

Then this is a governance and visibility play before the bill grows. The audit produces a baseline (usage patterns, model selection, cache-hit rate) that becomes the foundation when usage scales. Better to install the meter before the waste compounds than after.

How does the performance pricing work in practice?

We measure your baseline spend before the engagement starts. Every month, we compare current spend to the baseline, net of our infrastructure cost. Our cut is a percentage of the savings, with a small floor. If the meter finds nothing, you have lost almost nothing. The incentive is aligned. Exact structure is scoped in the diagnostic.

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On-Prem & Sovereignty

When the right answer is no cloud AI at all: private, local, compliant.

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AI Architecture

The full intelligence layer: agents, knowledge platforms, and governance.

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Strategy

The diagnostic that surfaces what your operation actually needs.

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Engagement starts here

Find out what your AI is actually costing.

Book a diagnostic. We'll scope a 30-day audit and show you where the waste is, before you commit to anything.

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Not limited to what's listed. Every engagement starts by assessing what your business actually needs, and we build whatever it requires.