For CIOs & heads of AI

You can see what your AI costs. Can you see what it's doing?

Every employee can spend tokens. Almost no one can tell you whether that spend moved a single number. OTP is the operating layer where every person and every AI agent gets a seat, a number, and a real job — so cost finally connects to contribution.

SOC 2 in progress· HIPAA-aligned· EU data residency· Your API keys
Master scoreboard · Q3
LIVE
BPipeline created
$1.4Mon track
DReports auto-shipped
38/40on track
TCost per outcome
−22%watch
SK
Sarah Kim
Product Manager
Human
Value delivered92%
AR
Alex Rivera
RevOps Lead
Human
Value delivered78%
RA
Research Agent
Reports to Alex
Agent
$ spend → value3.1×
WF
Workflow Agent
Reports to Sarah
Agent
$ spend → value1.2×
The accountability gap

Your AI has a spend dashboard. It doesn't have an accountability layer.

You rolled out Claude, Copilot, a dozen agents. You can report the bill to the dollar. What you can't answer is the only question that matters to the board: did any of it move the number?

Spend ≠ value

You can tell me what each person spends on AI. You can't tell me whether they were more productive for it. That disconnect is invisible on a usage report.

Agents act unsupervised

Agents do work between meetings, call tools, and soon spend money — with no seat, no owner, and no one checking it's the right work. Usage looks like progress. It isn't.

Tokens are a vanity metric

Adoption measured in tokens is gameable — anyone can burn budget to look engaged. Real adoption shows up as a KPI that moved. OTP measures that one.

One operating system

Every human and every agent on the same chart, scored on the same numbers.

OTP puts your whole org on one accountability map. People feed their KPIs. Agents report theirs automatically — through an MCP snippet that pulls their role, SOPs, and results straight off Claude, n8n, or whatever they run on. Everything rolls up to your operating plan: the 3–5 year targets and the one number this quarter. Humans and agents drive the same goals.

A seat and a number for every agent

Each agent carries a role, a mission, a maturity level, the SOPs it owns, who it reports to, and the KPI it's accountable for — the same scorecard you hold a person to. When an agent stops earning its seat, you see it and retire it.

Visibility that rolls up

An agent that reports to your RevOps lead rolls up to you. You finally see the work happening two and three levels down — people, agents, and the dependency and accountability gaps between them — without asking anyone for a status update.

Source code you keep

Every agent's setup is housed in OTP. If the person who built it leaves, the agent doesn't — you re-seat it on someone else, or port a great one from one team to another. Institutional knowledge stops walking out the door.

Skate to where the work is going

You already know how to manage people. OTP runs that play and the next one at the same time — the one where the decision is "should this seat be a person or an agent?" You'll be making that call. Make it with data.

Governance & control

Built for the person who has to sign off on it.

The reason you're reading this far is that AI without governance is a liability. OTP was built upstream — the controls a CIO asks for are the product, not a roadmap promise.

Granular access controlDecide who sees what, down through the whole org. Hide the chart, a meeting, a KPI — it disappears from their screen, not just dims. You hold the keys.
Append-only audit loggingEvery change is recorded and immutable. How data is created, deleted, and retained is documented — in plain English on the trust page, not buried in a contract.
Private data — no model trainingTurn on private mode and we don't learn from your data, ever. Issues, KPIs, scorecards, learnings stay yours. You control who has access; we don't repurpose it.
EU data residencyHosted in the EU (Amsterdam) on PostgreSQL 16 + Cloudflare R2. SOC 2 underway; HIPAA-aligned infrastructure. Full data map on request.
Bring your own keys & cloudPlug in your enterprise API key — your negotiated rates, your minimums, your cloud. AI work runs on your account, not a reseller markup.
Model-agnostic & 1,100+ integrationsClaude, OpenAI, your own stack — OTP doesn't care. Install our MCP to pull agents in automatically, or use 1,100+ built-in integrations and an open API.
See the full trust & security model →
Not a better tool — a new layer

It's not a better Salesforce or Workday. It's the category they were never built to be.

SaaS was built to manage people. The next decade is people and agents working the same plan — and nothing you own today was designed for that. OTP isn't software-as-a-service. It's data-as-a-service for the org you're about to run.

— What a CIO saw 30 minutes into his first walkthrough

For operators & portfolios

The organizing function of a value-creation plan.

Ask a PE operator what one portfolio company can learn from another today and the honest answer is: nothing structured. OTP turns that into an asset. Connect every company into one view, compare how they actually operate — seats, agents, SOPs — and push proven best practices from one into the next.

Master KPIs roll up

Every entity's numbers cascade into one portfolio scoreboard — 22 companies or 300 sub-orgs — set up automatically, AI-driven, not by hand. From 140 people to 3,000, same view.

Compare operating systems

Look at how one company runs versus another at the operating level — not the financials, the machine. Spot what's working and ingest it where it isn't.

A portfolio intelligence layer

Best practices, agents, and whole operating systems become portable assets across the book — the cross-company learning that's worth zero today, finally captured.

Start contained

Start with your leadership team. Expand when it earns it.

You don't roll this out to 140 people on day one — and you shouldn't. The smart path is contained: prove value at the top, then let it cascade as people pull it in.

1 · We load it for you

White-glove onboarding. We import your org chart with contacts, upload your SOPs into each person's file, and invite your leadership team — you don't build a thing.

2 · Humans first, then agents

Start with people on the system you already run (EOS, Scaling Up, or your own — 180+ meeting templates). Add agents as seats once the chart is real.

3 · It cascades on its own

Leaders add their teams; people add below them, never above. Adoption spreads by pull, not push — and you watch who's really moving the needle.

See it running on your own org.

A 30-minute walkthrough on your chart. We'll show you cost-to-contribution for humans and agents on day one — and exactly where your accountability gaps are.

We run our own company on these exact agents — live, on this platform. We'll show you ours, working.