Do you know
how your company
uses AI?
Metermark shows you what your AI spend actually buys, across every tool your people use. Who gets the most from it, what kind of work it does, and where the rest of the company can catch up. No transcripts stored, ever.
Adoption is no longer the story. Value is.
More than 90% of developers use AI every day. Vendor dashboards only see their own tool. Engineering-intelligence tools only see engineers. Nobody can tell a CEO what all that usage is actually doing.
Enterprise gen-AI spend in 2025, up 3.2× year over year. The money is committed. The accountability question is arriving on schedule.
Menlo Ventures
of gen-AI pilots show no measurable P&L return, despite $30–40B invested.
MIT, State of AI in Business 2025
of decision-makers can identify a specific financial outcome from their AI investments.
Gartner
of employee AI time savings are lost to rework. Only 14% see consistent net-positive outcomes.
Workday, January 2026
And no engineering dashboard can see them. In most companies the heaviest AI users sit in product, design, marketing, and operations, where none of today’s tooling looks.
Security tools read everything and sell fear. Vendor dashboards are free and single-vendor. Engineering tools stop at the engineering org. The question every executive is about to be asked, what did we get for it?, needs a neutral layer that sees every tool, understands what the work was, and covers every kind of worker.
Know what your AI spend buys.
Metermark joins the usage every AI vendor already reports into one cross-vendor account of spend, work, and outcomes. Three parts, none of them in your critical path.
Every tool, one ledger
Lightweight collectors tap the telemetry your AI tools already emit — Claude Code, Cursor, Copilot, ChatGPT Enterprise, Bedrock and more — and normalize it into one record per session. Who, when, which tool, which model, what it cost, what shipped.
Understood inside your boundary
Prompts are read where they already live, on your machines or in your cloud, labeled by the kind of work they represent, and discarded. Only the labels leave. About 2 KB per session; a 500-person company produces megabytes a day, not terabytes.
Never in the request path
No gateway, no proxy, nothing between your people and their tools. No latency story, no uptime story, nothing to break on a Tuesday. Metermark reads what already exists and answers in plain English.
One record per session. Fixed spine, extensible leaves.
The spine — identity, time, cost, outcomes — never changes, so records stay comparable across vendors and later power benchmarking. The leaves are yours: your project mapping, your definition of work-related, your labels, your custom fields. Corrections made in the dashboard feed back into the classifier, so accuracy is yours too.
- who
- user (SSO), team
- when
- start, duration, active time, turns
- where
- harness · surface · auth mode
- what
- models, usage in/out/cached, cost, tool mix
- context
- repo or project, languages, branch
- derived
- kind of work · topics · work-relatedness ← computed at the edge, content discarded
- outcome
- commits, PRs, lines added and removed
- custom
- namespaced, typed, size-capped fields
The questions you can finally answer.
Classifying what people ask AI to do — never storing what they said — turns a usage bill into a map of how your company actually works.
Spend by kind of work
Last 30 days · every tool · one company
- Building features34%
- Fixing bugs19%
- Research14%
- Writing & docs11%
- Product planning9%
- Data analysis7%
- Learning4%
- Other2%
Who gets the most out of AI, and what do they do differently?
Find the people whose AI sessions actually ship. See how they work — how they brief, how they iterate, which tools they reach for — and turn it into a playbook for everyone else.
Which teams are AI-native, and which need a hand?
Spot the teams still using AI like a search box. Give them a concrete path to how your best people work, and watch the gap close quarter over quarter.
What did the money buy, by team, project, and kind of work?
Features, fixes, research, documents, planning. Across Claude, Cursor, Copilot, and ChatGPT. One ledger, one number, and the ability to defend it in a renewal review.
What is the rest of the company doing with AI?
Product, design, marketing, ops, finance. The people engineering tools can't see are often your heaviest users, and your biggest upside.
What can we see, and what can't we?
Every gap stated plainly. “91% of your AI usage has semantic coverage” is a real number. “Total visibility” isn't, and we won't claim it.
Which team spends the most on research?
Ask in plain English. Metermark answers with the numbers and shows the query it ran, so finance and engineering argue about the same figure.
Every tool. Honestly labeled.
Coverage is a first-class feature. Each surface is qualified by what its vendor actually exposes — per auth mode, not per logo — so the number on your dashboard is one you can repeat to your board.
Verified against live vendor documentation, September 2026. Vendors change; the map is versioned and re-verified.
No transcripts stored. Ever.
Built for enablement, not oversight. People who feel watched leave, and regulators agree: the EU AI Act treats workplace AI monitoring as high-risk. The architecture is the promise.
Transcripts stored. Prompts retained. Responses kept. Metermark never receives the content, so it can never be asked for it, subpoenaed for it, or breached for it.
Classified at the edge, content discarded.
Prompts are read inside your boundary, labeled, and dropped. What leaves is a handful of words per session, and the label is barred from quoting content, names, secrets, or links.
Aggregate by default.
Team-level views first. Person-level detail is role-gated, audit-logged, and switched off below a minimum group size. Shipped as admin policy; offered to legal as a feature.
Your data stays in your store.
Want to reclassify later with new labels? Keep redacted content 30 to 90 days in your own bucket and the job runs there. Compute goes to the data. The sensitive asset never lands on our books.
Nothing in the request path.
No gateway, no proxy, no agent sitting between a person and their tool. We can't read what we never receive, and there is nothing of ours to go down.
Priced per covered user. Paid for by what it finds.
At a 300-person company with 150 AI users, Growth runs about $27k a year, roughly 15% of the AI spend it explains. The goal is for Metermark to pay for itself out of the misattributed spend it uncovers.
up to 20 covered users
- One team, one collector
- 30 days of history
- Spend across the tools it sees
- The coverage map
Adoption counters are free everywhere. The understanding starts at Growth.
Start freeper covered user / month · $12k minimum
- Every collector, every vendor
- Kind-of-work classification at the edge
- Cross-vendor rollup and spend attribution
- Power-user and team upleveling views
- Questions in plain English
Guided self-serve. An IT admin finishes rollout in a day or two from the runbooks.
Get an assessmentper covered user / month
- Compliance-API sources: ChatGPT Enterprise, Claude Enterprise
- Classifier deployed inside your VPC
- Bring-your-own-bucket retention
- SSO, SCIM, audit log, API export
- Hands-on rollout
For cloud-log and compliance-grade sources, deployed with you.
Talk to usGet a free AI usage assessment.
Point Metermark at one team for a week. You get a report of what your AI spend bought, who your power users are, and where your coverage gaps live. No transcripts stored. No contract.