Your enterprise users, your developers and your AI agents each use AI differently — and each needs a different intervention. Vantable traces all three, surfaces what matters to the leader who owns them, and delivers the nudge, the coaching or the fix that changes the outcome.
Built for regulated enterprises that take AI adoption seriously
Seats are provisioned, agents are in production, dashboards exist — and the question every executive asks next still goes unanswered.
A knowledge worker, a developer and an autonomous agent generate completely different signal. Tools that pool them into one adoption number answer nobody's question — least of all the person funding the spend.
When usage rises while quality slips, a dashboard shows two lines crossing. It does not name the person, the repository or the agent workflow to change — or how you would know the change worked.
Training is generic, coaching is manual, and agent quality is assessed after an incident. Without per-population evidence and controlled measurement, no one can prove which intervention moved the number.
Every AI action in your enterprise is taken by one of three actors. Vantable instruments each of them, analyses each in its own terms, and intervenes in the way that actually works for that population.
Thousands of seats across Claude Enterprise, ChatGPT Enterprise and Copilot Chat — most of them nowhere near fluent, some of them dormant, all of them costing money.
Assistant usage that sits inside the delivery system — so it can be measured where it counts: first-run CI success, review iterations, cycle time and cost per merged pull request.
Your own agents, running unsupervised in production. Every step, tool call, retry and hand-off traced — evaluated before release and health-checked after it.
Signal is captured read-only, refined into one shared ontology, scored against the KPIs each leader actually owns, then carried through to a verified outcome. Detection is where the work starts, not where it ends.
Enterprise tool usage, audit logs, delivery events and full agent run traces — read-only, no raw prompts.
Curated data products and one ontology of people, agents and workflows — so a seat, a repository and an agent run become comparable.
The KPI engine ranks each finding by population, cost and evidence strength, and states the trade-offs of every option.
A private nudge, a digest mail, a coaching plan, a model-mix change or a blocked release — routed to whoever can act, after human approval.
Measured against that population's own baseline. "Inconclusive" is a reported outcome, not a silent one.
The architecture underneath. Five layers on one telemetry spine, with a dedicated lens for each persona.
Read bottom-up — signals land in the core, become one lens per persona, and surface as approved action
Claude Enterprise, ChatGPT Enterprise and Copilot Chat seats across the business. Vantable sees who has become fluent, who has quietly gone dormant, and who is paying premium rates for routine work — then coaches each of them privately.
Product · Vantable WorkforceCopilot, Claude Code and CLI assistants sit inside the delivery system — which means their value is measurable where it counts: first-run CI success, review iterations, cycle time and cost per merged pull request. Not suggestion counts.
Product · Vantable EngineeringAgents built in-house now make decisions in production — retries, tool calls, hand-offs and spend, unsupervised. Instrumented with the Vantable SDK, every step is traced, evaluated before release, and health-checked after it.
Product · Vantable SDKRestores the eval gate · est. saving $18.4k/mo · quality delta within tolerance on a 500-task eval.
Approve one-clickThree moments from a normal Vantable day — a threshold crossed, a private nudge delivered, a fix approved and verified. Nobody has to remember to check anything.
Weekly active seats with a 7-day rolling average — the KPI engine flags the moment adoption crosses target.
Every person gets their own morning digest — their usage, their wins, one tip. Never copied to a manager.
Every finding becomes a proposed fix with trade-offs. A human approves; Vantable executes and verifies.
Est. saving $41k/yr · inactive 28+ days · re-activation nudge already sent twice, no response.
Approve one-clickThe Vantable Agent sits on the same telemetry — answering cost-per-outcome questions in plain language, and queuing remediations that execute only after a human signs off.
Here are your most efficient teams this quarter, determined by comparing AI spend with merged pull requests. Illustrative data — your view names your teams.
Team-level view shown by default. Individual cost-per-outcome is available only where your organisation’s privacy policy permits it.
An interactive preview with illustrative data — anonymised teams, indexed values, directional trends. Your dashboard carries your real names and numbers, visible only to you.
The same verified data answers a different question for each audience — no generic dashboard stretched across all of them.
Vantable is not a surveillance tool. The data boundary is explicit, published, and enforced by role.
People see their own usage, coaching, and progress. Admins see organisation- and team-level patterns — private coaching is not visible to them by default. Every metric ships with its definition and its known limitations.
Connect your enterprise AI tools and delivery systems read-only, then add the Vantable SDK to instrument your own agents.
Vantable backfills history for all three personas and publishes a data-coverage report — including what it cannot see.
First findings arrive with evidence strength, affected population, and a proposed measurement plan.
Approve delivery. Nudges, coaching and fixes go out; every outcome is verified against baseline.
Three ways to start: a 30-minute walkthrough on your own questions · a read-only 30-day pilot on one business unit · a spend-recovery assessment on your current AI bill.