Replicate every worker with AI.

It shadows a worker, learns the job, then takes over on the same laptop.
Indistinguishable from a human.

zoral.ai

Demo

Watch Zoral replace a real worker.

One recording. Three phases. Full replication.

The last mile problem of AI automation

Every AI tool requires a million integrations. We took the opposite approach. Instead of bringing human tools to the AI, bring the AI to the human tools.

Traditional AI Automation

Connect Zapier.

Set up MCP servers.

Install the Hubspot skill.

Wire up n8n.

Configure OAuth tokens.

Maintain webhook routes.

One API changes and the whole thing breaks.

Zoral

Same computer. Same apps. Same email. Same browser.

If a human can use it, the agent can use it.

This is not another AI vertical tool. We're not building an AI SDR, or an AI customer support agent, or an AI data entry clerk. We're building the system that can become any of them.

How it works

Three stages, one system. Observe the worker, take over with a safety net, then run the role alone.

ObserveSupervisedAutonomous

Observation

Stage 1

Zoral installs on the worker's laptop and shadows them for a short period. On every action it predicts what it would do, watches what the human does, and learns from the difference.

Runs on the real laptopLearns from every action

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Supervised

Stage 2

It takes over, but asks for approval before each externally-visible action. Internal work runs freely; anything that leaves the building waits for a human yes.

Approval before external actionsInternal work runs freely

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Autonomous

Stage 3

It runs the role unsupervised, on the same email, chat, and tools the human used. Colleagues interact with it exactly as they did the person, and it keeps learning on the job, indefinitely.

Indistinguishable from the humanSame tools, zero setupKeeps learning on the job

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But how does it remember, and get better?

The gap

Every other agent forgets.

Context windows and RAG scaffolds reset every session. With no long-horizon memory, an agent never accumulates experience, so it never becomes a real worker.

Zoral learns into its weights. Weights don't reset.

The architecture

A new architecture, mapped from the brain.

Everyone else stops at the harness: prompts and RAG bolted onto a frozen model. Zoral learns inside the model's weights, one layer below where competitors stop, which is what gives it the long-horizon memory they lack.

The human brain

The system we are copying, the only thing we know can learn a job and keep learning.

A functional model

We decompose it, function by function, into a control-theory model of what each part computes.

Trainable networks

Each function becomes a network, distilled from pretrained models, so nothing trains from scratch.

Not vague inspiration. The brain, decomposed and rebuilt step by step. This is what makes true long-horizon continual learning possible, and every deployment compounds into data a new entrant cannot fast-forward.

Replicate first. Optimize second.

Foundation

Human behavior (replicated exactly)

Optimizations

Terminal commandsAPI callsAI vertical tools

Same I/O surface as the human

Once the agent perfectly replicates what the human was doing, optimizations apply on top. Why type in TextEdit when you can write a file with one terminal command? Why click through a UI when you can hit an API directly? Those AI vertical tools (your CRM, your analytics platform, your outreach tool) become part of the optimization layer. The agent uses them the same way a human power-user would.

From the outside, the system still looks like a human worker. Imagine being at work and having no idea whether your coworker is an AI or a human.