Core offering

An AI employee that knows your process

A supervised worker trained on your context — not a generic chatbot bolted to a help center.

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A role, not a widget

Most "AI" you can buy is a chat box that answers from a public manual. An AI employee is different: it is trained on your actual process — your definitions, your data sources, your escalation rules — and it owns a recurring job. Monitoring a set of dashboards. Triaging an inbox. Running a research sweep and writing the digest. Chasing the follow-ups that fall through the cracks.

It does the role the same way a reliable hire would, and it reports what it did. Because it is scoped to a role, you can judge it the way you judge a person: did the report go out on time, were the alerts right, did anything slip. That is a far more useful question than "is the chatbot smart."

Active worker — operations role
Inbox triage
Classifying 3 new messages
Running
Follow-up scheduler
2 reminders queued for today
Queued
Research digest
Delivered 08:00 this morning
Done

What's included

Every AI employee build ships with these

Process training

Trained on your real documents, data sources, and definitions — not a generic model with your logo on it.

Recurring ownership

Owns a scheduled job with a clear definition of done — not a widget you prompt on demand.

Full supervision

Every action is logged, visible, and can be overridden. Corrections feed directly back into the worker's behaviour.

Tool integrations

Connected to the tools your team already uses — inbox, CRM, Sheets, Slack — with permissions scoped to exactly what the role needs.

Ongoing re-training

Maintained and updated as your process evolves. Self-healing: when a run fails, the worker proposes a fix for human review before it applies.

Secure hosting

Deployed on Fly.io, state on Supabase — EU data residency, encrypted at rest. BYOK or fully managed; credentials never in plaintext.

How it works

Step 1

Free ops audit

We map the role you want to automate. If it is not a good fit for AI, we say so. If it is, we scope it clearly before writing a line of code.

Step 2

Context and training

We ingest your process documents, data sources, and escalation rules. The worker is trained on what is actually true in your operation.

Step 3

Supervised launch

The worker goes live with oversight on. You watch the first runs, approve the outputs, and give corrections. Corrections feed back into the worker.

Step 4

Ongoing and self-healing

The worker runs on schedule, logs every action, and flags edge cases for human review. When it encounters a failure, it writes the fix back into its own behaviour.

Self-healing behaviour

Every AI employee we build is designed to improve from its own mistakes. When a run produces an unexpected result, the worker logs the failure, identifies the root cause, and proposes a correction — which goes through human review before being applied.

The result is a worker that gets more accurate over time rather than one that degrades silently. This is not a feature you toggle on — it is part of the architecture from day one.

Your keys, or ours

Every AI employee deployment supports two key-management models. Unlike other AI agents, we do not store your keys in plaintext — credentials are encrypted at rest and scoped to the minimum permissions the role needs.

GDPR compliant Encrypted at rest EU data residency

Common questions

AI Employees FAQ

Simple roles — inbox triage, daily digests, single-tool monitoring — typically go live in 5–7 business days from the audit call. More complex roles that span multiple tools or require custom integrations take 2–3 weeks. We scope it at the audit and commit to a date before writing a line of code.
At minimum: a description of the role, access to the process documents the role relies on (SOPs, definitions, templates), and read access to the tools it will connect to. We do not need a full technical spec — the audit call is where we extract that. Most clients share a Google Drive folder and a 30-minute walk-through.
When a run produces an unexpected output — a classification that doesn't match your rules, an integration call that fails, an edge case the worker hasn't seen — it logs the failure and identifies the likely cause. It then drafts a correction and surfaces it for your review. You approve, reject, or adjust the correction; the worker learns from that decision and applies it to future runs. Nothing changes without a human sign-off.
With BYOK you supply the API keys for the model provider and any third-party tools — we never hold credentials you don't want us to hold. With fully managed, we provision and rotate keys on your behalf; they are stored encrypted at rest in Supabase (EU region) and never appear in source code or config files. Both options are GDPR-compliant. BYOK is preferred by clients with strict data governance requirements; managed is simpler operationally.
Off-the-shelf tools are general-purpose and require a human to prompt them every time. An AI employee is trained on your specific process, runs on a schedule without anyone prompting it, writes back what it did, and gets corrected over time. It is the difference between a search engine you query and a junior analyst who shows up and does the job — and who improves because they learn from corrections.

Start with a free audit

Tell us the role you wish you could hire for. We will tell you, plainly, whether an AI employee can do it well.

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