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Platform · AI

AI that does a named job and shows its working.

It proposes the audience, drafts the message, checks every send and explains the result, from your own data. Every proposal shows why, the figures are computed rather than guessed, and you accept or reject each item.

THE PRINCIPLE EVERY CAPABILITY SHARES

Facts by rule. Judgment only where it is needed.

Deterministic where a rule can prove it. The model only where judgment is genuinely required. Never the last step, never doing arithmetic.

Code establishes the facts and the constraints. The model contributes the part that needs language or ranking. Code validates what comes back before it reaches you. Each capability lives in the screen where the work happens, every run is recorded, and you accept or reject items individually.

Built in

Each does a named job and shows its working.

Agent 01 · Predictive Segmentation

Proposes the audience

Candidate audiences are generated from your real data first. The model ranks and names them and writes the reason. It cannot invent a condition. Rejected candidates are shown with the reason they were refused.

  • From your real data first
  • Ranked, named, with a reason
  • Refused candidates shown, with why
Agent 02 · Copy Writer

Writes the message

Drafts channel-aware variants grounded in a brand voice profile you maintain, in the way your recent templates actually read, and in the audience targeted. Every draft passes a deterministic check before you see it.

  • A variant per channel
  • Your brand voice, your templates
  • Checked before you see it
Agent 03 · Template Compliance

Checks the template, per channel

Four layers in front of a send, from most certain to least: hard-coded personal data and missing opt-out, WhatsApp structural rules, your own keyword blocklist, and only then the model, for tone and restricted claims. A deterministic failure blocks.

  • Personal data and opt-out
  • Channel rules and your blocklist
  • Tone last, and only as judgment
Agent 04 · Campaign Insights

Explains the result

Every ratio is computed by code. The model writes the narrative and three next moves, each naming the capability that would carry it out.

  • Every ratio computed
  • A written narrative
  • Three next moves, each actionable
Story Generation · Four agents, two gates

A complete Story from a single topic, and two gates before anyone sees it.

Give it a topic. A Creative Director turns it into a brief: what the story is for, what it should say, how it should be shaped. A reviewer checks that brief against fixed rules first and a creative read second, and sends it back if it falls short. Only then does a Story Generator build the pages. A final reviewer checks the result the same way, hard rules first, judgment second. Nothing reaches your team that hasn't passed both gates.

Asklytics is described where it lives: Analytics. Stories themselves, and how they publish, are on the Stories page.

YOUR OWN AGENTS, ON THE SAME TERMSOPEN STANDARD · MCP

Bring your own agents.

Connect any compatible agent through xNotify's MCP Server and it can work the platform through 112 tools, only within the scope you grant. Your agents can publish Stories too, under the same scope.

An authorised agent can launch a send, and a sent message cannot be unsent. That is why scopes, dry-run and idempotency exist, and why deletion stays a human action in the portal with a named person against it. The worst an agent can do is create work, not lose data.

  • 112 tools an authorised agent can use to build a segment, launch a campaign, run a journey, submit a template, send a message and publish a Story
  • Filtered to the scope you grant
  • No tool deletes anything
  • Repeated calls replay rather than repeat
  • Any write can be dry-run first
  • Access can be revoked for one agent, one subject or the whole tenant
STATED PLAINLY

What the model sees, and where it runs.

  1. 01

    Personal data never reaches a model

    Three independent filters sit between your data and any model, all before the call. The model that produced each result is recorded.

  2. 02

    Where inference runs

    AI capabilities use external model providers. Hosting the platform on your premises or in your country does not by itself change where an enabled AI capability processes information. If you require in-country or self-hosted inference, raise it during evaluation; the provider layer is designed to be extended, and that is scoped work.

  3. 03

    Every run is recorded

    Each capability lives in the screen where the work happens. You accept or reject items individually, and the record shows what was proposed, what was refused, and why.

The pilot

One use case. Six to eight weeks. A number you agreed first.

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