Engine

The engine your team runs on.

A team-facing AI assistant inside your helpdesk, and a Living Knowledge library that maintains itself from every accepted draft, override, and edit. Documentation ops drop to near-zero.

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Feature 01 - Living Knowledge

A library that maintains itself.

Every source your business runs on - tickets, internal docs, Slack, Confluence, Google Drive, support recordings, websites - pulled into one self-maintaining library, the knowledge base that never goes stale. Non-ambiguous. Always current.

Documentation ops drop to near-zero. The Library is what the Customer Agent answers from, what the Team Assistant drafts from, and what the Help Center is generated from - one source of truth, three surfaces.

It is also very hard to poison. Before any source enters the Library, it passes through guardrails that screen out contradictory, manipulated, or planted content, so none of it ever reaches an answer.

The system

Three senses, one mind.

Living Knowledge, Living Memory, and Living Context - the three senses every UNLESS agent runs on. One mind, three feeds: what it knows, who it's talking to, where it's acting.

01

Living Knowledge

What the agent knows. Your business, continuously restructured and rewritten into one non-ambiguous Library.

02

Living Memory

Who the agent is talking to. Preferences, history, sentiment, goals - private per customer. Unites structured data and working memory.

03

Living Context

Where the agent is acting. Your current page, CRM, billing, ERP, support tools, custom APIs - connected and aware.

Inbox

What to do, when.

You don't watch the dashboards. The Inbox watches them for you and surfaces what needs human judgment.

If a signal fires

Take action.

A drop in usage, a retention risk, an upsell trigger - the Inbox routes the play to the right operator the moment the signal fires.

If content needs your judgment

We tell you.

Knowledge conflicts, draft articles, tone rephrases - they queue up for one click of Accept or Deny. No content ops, no email chain.

If your next maturity step is in reach

We point it out.

A cell on the AI-maturity matrix closes - Automated for Retention, AI-First for Acquisition - and the Inbox names what to ship next.

The loop

Four phases that close it.

Each phase is its own dashboard. Train fills the library, Test rehearses every role before a customer sees it, Deploy ships behind one agent across every moment, Analyze proves the value - and feeds the next round of training.

01

Train

Your knowledge, always current. Your procedures, always ready.

02

Test

Every role, every skill, before a customer sees it.

03

Deploy

One agent. The whole journey. Memory across all of it.

04

Analyze

Performance. Value. Maturity. All visible. All live.

Most AI tools sell features. Unless sells a system that maintains itself.

Compliance

Built for Europe.

EU data residency by default. PII filtered and tokenized at the gateway. Per-decision audit trail.
GDPR EU AI Act DORA
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Frequently asked questions

What is the Engine?

The Engine is the platform underneath the Customer Agent and the Team Assistant. It is a Living Knowledge library that maintains itself, plus the Train, Test, Deploy, Analyze loop that closes after every interaction. The Engine is what makes the agent learn on a loop instead of answering one question at a time.

What is Living Knowledge, and why is it different from a knowledge base?

Living Knowledge is one self-maintaining library built from every source your business runs on, from tickets to docs to Slack to Confluence to recordings. It restructures and rewrites itself when the underlying truth changes. Documentation operations drop to near-zero, and one library feeds the Customer Agent, the Team Assistant, and the Help Center at once.

What are the three senses?

Living Knowledge, Living Memory, and Living Context. Living Knowledge is what the agent knows about your business. Living Memory is who the agent is talking to, with preferences, history, sentiment, and goals private per customer. Living Context is where the agent is acting, connecting it to your CRM, billing, ERP, support tools, and custom APIs.

What is the Inbox?

The Inbox is today's briefing inside the dashboard. You do not watch the dashboards; the Inbox watches them and surfaces what needs human judgment: a usage drop, an upsell signal, a knowledge conflict, an AI-maturity step in reach. Each item resolves in one click of Accept or Deny.

What are the four phases of the loop?

Train, Test, Deploy, Analyze. Train fills Living Knowledge and Living Context from the sources you connect. Test previews every role and simulates every skill before a customer sees it. Deploy puts the agent in front of customers across all four moments. Analyze shows performance, value, and AI maturity, then feeds the next round of training.

What does Train do?

Train turns your existing content into Living Knowledge and Living Context. Help center articles, ticket logs, product docs, Slack channels, recordings, CRM fields, all of it pulled into one library the agent can read without ambiguity. The Help Center is generated from the same library, so two libraries that disagree become one library that agrees with itself.

What does Test do?

Test lets you preview every role, simulate every skill, and audit every decision before a customer or auditor sees it. You run a control set of questions, replay real interactions, and read the audit trail in plain language. Your DPO and your auditor see exactly what you see.

What does Deploy do?

Deploy puts one agent in front of customers across all four moments. The agent carries Living Memory and Living Context from one moment to the next, so a visitor who became a customer never has to introduce themselves again. No cold opens, no four separate bots.

What does Analyze show?

Analyze shows performance, business value, and AI maturity in one view. You can see deflection, resolution time, CSAT, and revenue impact by moment, by topic, and by segment. The same dashboard tells your CFO and your DPO what they each need to hear, with the numbers backed by the audit trail.

Which AI models does Unless use?

Unless runs a constellation of EU-hosted foundation models from providers like Anthropic, Mistral, OpenAI, Google, Cohere, and Amazon Titan, rather than betting the platform on one. The constellation lets us pick the right model for the job, balance load, and switch in real time if regulation or vendor terms change. Customer data is never used to train public models.

See the loop in your data.

Walk the same four phases with your own knowledge, your own customers, your own numbers.

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