The managed platform for AI agents

Your first agent, working in two weeks

Don't start from scratch — the platform already exists.

Bring us the workflow you've been putting off.

Integrations
Memory
Scheduled routines
Evaluations
Spend management
Observability
Access control
Self-learning

Scope your first agent See the technical details

Everything your agent needs

Build the agent that differentiates your product, not the platform under it.

Integrations

The systems your workflow already lives in, each Connection scoped to an identity, an authority, and a lifecycle.

Named connectors for the systems customers depend on
MCP, REST, GraphQL, SQL, and webhooks reach the rest
Your own product connects like a first-class system

Multi-dimensional memory

The agent remembers what it should — and only where it should.

Per-person, per-conversation, and per-Tenant scopes
Recall never carries a fact into a wider room than it came from
Grounded in live reads, with citations and freshness

Multi-surface

Slack, email, SMS and RCS, or inside your own product — including group chats where several people are talking at once.

Every speaker attributed, in a channel or a thread
Threading, reactions, and live progress
A new surface is a registration, not a rebuild

Scheduled routines

Recurring and event-driven work that pushes to you, so nobody has to remember to ask.

Schedules with time zones, DST, quiet hours, and a missed-run policy
Provider events, deduplicated and durable
A check that finds nothing costs nothing

Evaluations gate every release

Any change to a model, prompt, Tool, or budget becomes a candidate that has to earn its promotion.

Versioned suites, fixtures, and calibrated graders
Canary rollout with automatic abort
Rollback is a release, not a redeploy
Model releases qualify like any other change — no silent fallback

Spend management you can see

Tokens, cost, and latency recorded per Run and rolled up by Organization, Tenant, and workflow.

Reasoning tokens counted apart from visible output
Budgets, alert thresholds, and spend caps
Provider usage sits inside the fee, within your written envelope

Model and provider usage included — no per-token bill.

Observability you own

OpenTelemetry throughout, so every Run traces into Cantora's own tracing by default and can export to the backend you already run.

Exports to any OpenTelemetry collector
Content-free spans by default
Complete Run Capture when you need the exchange itself

Access control, stated plainly

Two walls hold every read: your Organization and each customer Tenant. Every query carries both scopes, and every data path is tested by trying to cross them — and failing. Agents read; they never write, and that is a decision rather than a gap. Isolation is logical on shared infrastructure, and Cantora publishes that limit, and every other one, beside the control it qualifies.

Cross-Organization and cross-Tenant denial tested on every path
Least-privilege, read-only Connections, revocable at any time
A fresh sandbox for every program, destroyed when it finishes
One short-lived, minimum read credential per approved operation — refresh tokens and client secrets stay on the host
The sandbox's boundary is the credential's authority and its lifetime, not a network filter
An append-only audit log behind every management change

Ask for the security pack

Self-learning

Cantora recognizes when iterative work has become stable, offers to remember it, and pairs the saved program with memory about when to reach for it — so your agent's work compounds, and the thousandth answer costs less than the first. It builds on evidence the platform already retains, because every Calculation keeps the exact program that worked.

You own the workflow and all the data. Cantora runs the platform underneath

A managed platform — not a framework, not a library. Launching an agent is a data operation, so a new workflow needs no customer-specific deploy.

You own	Cantora runs
The workflow and what the agent should do	The runtime that executes it
Business context and source data	Integrations, context assembly, and grounded retrieval
The acceptance boundary and the definition of success	Evaluations, release gating, canary, and rollback
The business result	Everything under it — quality, latency, reliability, and cost

The split is why the platform side keeps getting better: Cantora's whole engineering effort goes into the layer under your agent — integration coverage, response quality, the memory architecture, the token cost of every answer — and an improvement that would change your agent's behavior still earns its release first.

What you are really buying

The prototype takes a sprint. The platform under it takes a year — and every sprint spent rebuilding it is a sprint your product didn't get.

How Cantora runs it

Code is the primary tool for structured-data work

How did this week compare with last week?

Revenue is up +$17,653.55

the program Cantora wrote1,023 records read

// Runs once, in a fresh sandbox, under a read credential
// scoped to this Tenant by the provider itself.
const orders = await commerce.orders.list({
  placedAfter: "2026-07-14",
  placedBefore: "2026-07-28",
});

return Object.entries(Object.groupBy(orders, byIsoWeek))
  .map(([week, weekOrders]) => ({
    week,
    orders: weekOrders.length,
    revenue: sum(weekOrders.map((o) => o.total)),
  }));

Ran once in a fresh sandboxTenant-scoped read credential, finite time and resources, destroyed after the call.

what the model readthe whole answer

[
  { "week": "2026-W29", "orders": 512, "revenue": 184230.55 },
  { "week": "2026-W30", "orders": 511, "revenue": 201884.10 }
]

Ask a question about a thousand records, and most platforms make the model read all thousand. Cantora writes a program instead: the model reads the result, not the records — and the program, the input, the authority it ran under, and the result are all retained for inspection.

95.2%Median reduction in total tokens across six measured structured-data workloads, ranging from 75.3% to 99.3%.

Deterministic where it should be — a sum is a sum — and inspectable after the fact. Pull the answer apart to see exactly what ran.

See the full code operation, and where the boundaries sit.

Fewer tokens is the smallest part of it

The saving grows with the data — exactly where context-stuffing gets most expensive. And because usage sits inside the fee, within your written envelope, every token removed is Cantora's saving to earn and your price to keep flat.

Correctness

Aggregation happens in code, deterministically — a sum is a sum, and it does not drift between runs the way a model's arithmetic does. The retained program shows exactly what was computed.

Better answers

A compact result leaves the context window for reasoning, and a model that reads less of what does not matter attends better to what does.

Speed and cost

Fewer tokens is less for the model to read and less for it to generate, so the answer returns sooner and costs less to produce.

Evidence

Cantora retains the exact program, the input, the authority it ran under, and the result. A reviewer inspects real logic, not a model's account of it.

The difference

This is the difference between an explanation and evidence.

Bring your first agent, or one whose cost, quality, or release behavior has become harder to manage than the workflow itself. See where Cantora earns its keep, or how it works underneath.

Built for the teams who run on it

The layout is real, and the names are not — cleared customer quotes replace these before they carry any weight.

We budgeted two quarters for the platform under our first agent. That work just wasn't there anymore — we spent the time on the workflow instead.

Maya TorresVP Engineering, fintech operations platform

Every change our agent ships has already earned it against our own cases. Rollback being a release, not a redeploy, is the control I didn't know I was missing.

Dan WhitfieldCTO, field-service software company

The answers come back small, sourced, and right — and the bill stays inside the envelope we agreed to.

Priya ShahCOO, property operations platform

Get started

Scope your first agent

Bring one bounded workflow, the systems it needs, and examples of a good answer. Cantora replies with the technically useful next step.

Scope your first agent