Introduction

Every customer interaction is an opportunity. The challenge is picking the right offer from dozens of options, personalizing it for the individual, and delivering it through the channel most likely to convert — all in milliseconds. KaireonAI is a Next-Best-Action decisioning platform that does exactly this, combining data ingestion, offer management, ML scoring, and real-time ranking into a single decisioning system.

One API call returns the single best offer or message for a specific customer in the moment — ranked by your goals, filtered by your eligibility and contact rules — and it learns from every outcome. KaireonAI is a proprietary, fully hosted SaaS. Start free on the hosted Playground, then move to a paid plan when you are ready to go live.

Why teams pay for it: more revenue per interaction; live in weeks, not years (it is a focused decisioning API, not a seven-figure suite); it improves itself from every outcome; and every decision is governed and explainable.

The Problem KaireonAI Solves

Most companies have offers, campaigns, and messages spread across disconnected tools. Email marketing knows about promotions. The app team has banners. The call center has scripts. Nobody is coordinating which customer should see which offer when. The result: customers get irrelevant recommendations, miss time-sensitive offers, or get bombarded on every channel at once. KaireonAI replaces this fragmented approach with a single decision engine that evaluates every eligible offer for each customer, scores them using ML models, and returns the best options ranked by a combination of propensity, business value, relevance, and priority.

See It in Action: A Retail Rewards Program

Imagine you operate a retail rewards program. You have 10 offers — BOGOs, discounts, and informational messages — and 6 channels: web, email, mobile push, social, batch email, and manual outreach. When a customer opens the app, which offer should they see? Without KaireonAI: You show the same “20% off your next order” banner to everyone, regardless of whether they prefer one product line over another, already redeemed a discount today, or typically only respond to BOGO offers. With KaireonAI: The Recommend API evaluates all 10 offers for this specific customer, filters out anything they have already seen this week (contact policies), scores the rest using a trained model that considers their purchase history and income level, and returns the top 3 — personalized, ranked, and ready to display.

curl -X POST http://localhost:3000/api/v1/recommend \
  -H "Content-Type: application/json" \
  -d '{
    "customerId": "cust-001",
    "channel": "Mobile",
    "attributes": { "age": 30, "income": 75000 },
    "limit": 3
  }'

The response includes scored, ranked offers with creative content ready for rendering:

{
  "count": 3,
  "decisions": [
    { "offerName": "BOGO Signature Beverage", "score": 0.85, "rank": 1, "channelName": "Mobile" },
    { "offerName": "25% Off Merchandise", "score": 0.72, "rank": 2, "channelName": "Mobile" },
    { "offerName": "Earn 3x Points This Week", "score": 0.68, "rank": 3, "channelName": "Mobile" }
  ],
  "meta": {
    "totalCandidates": 60,
    "afterQualification": 12,
    "afterSuppression": 10,
    "afterContactPolicy": 8,
    "degradedScoring": false
  }
}

The sample retail dataset ships as built-in sample data. Load it in Settings > Sample Data and follow the full tutorial to build this from scratch.

Key Capabilities

Real-Time Decisioning

The Recommend API evaluates, scores, and ranks offers in under 200ms. Every request runs through enrichment, qualification, contact policies, ML scoring, and ranking — no pre-computation required.

PRIE Ranking

The multiplicative PRIE formula (Propensity x Relevance x Impact x Emphasis) produces a single score from four dimensions. A zero in any dimension eliminates the candidate — no irrelevant high-value offers sneak through.

9 Scoring Engines

From transparent scorecards (no training data needed) to neural collaborative filtering (learns latent preferences from interaction data). Start simple, upgrade without changing your Decision Flows.

Omnichannel Delivery

Email, push, SMS, in-app, web, WhatsApp, webhook, and direct mail — all managed from one platform. Contact policies enforce frequency caps per channel so you never over-contact.

Visual Pipeline Editor

Decision Flows are built on a drag-and-drop canvas with 16 composable node types across 3 phases: Narrow, Score & Rank, and Output. No code required.

Built-In Experimentation

Champion/challenger testing with holdout groups, deterministic traffic splitting, uplift calculation (z-test), and optional auto-promotion. Measure real impact before rolling out changes.

80+ Data Connectors

Ingest customer data from S3, GCS, Azure Blob, Snowflake, BigQuery, Databricks, Postgres, Kafka, Kinesis, Pulsar, SFTP, HTTP, and more — across batch, streaming, file, and HTTP source kinds. Build visual ETL pipelines with 19 built-in transform types.

Five Monitoring Dashboards

Operations, Business, Data Health, Model Health, and Attribution dashboards with real-time metrics, Prometheus integration, and actionable alerts.

LLM Explanations

Turn any decision trace into a written explanation in three modes — regulator, agent, or customer. PII-redacted, cached per tenant, audit-logged for regulator mode.

Governed AI Layer

A built-in assistant, Decisioning Autopilot, and Decision Sentinel work the setup for you — but under governance. Every write and every autopilot proposal is previewed and queued for approval; Sentinel can auto-pause an unhealthy flow. Nothing changes production without a human in the loop.

Platform Architecture

Module	What It Does	Key Features
Data Platform	Ingests and transforms customer data	4 working ingestion connectors (S3, GCS, Azure Blob, SFTP) of 80 registered types, entity schemas with real PostgreSQL tables, visual ETL pipelines
Decisioning Studio	Configures what you can recommend and how	Offers, categories, creatives, decisioning gates, contact policies, Decision Flows
Algorithms & Models	Scores and ranks candidates using ML	9 scoring engines, experiments, champion/challenger, auto-learning
Dashboards	Monitors everything in real time	Operations, business KPIs, data health, model health, attribution

Architecture Deep Dive

Decision engine internals, data pipeline design, infrastructure adapters, and security model.

Start Here

Try the Playground

Start a free trial and explore the platform instantly — no setup, no credit card, no commitment.

Quickstart

Make your first recommendation on the free tier in five minutes.

Retail Rewards Tutorial

Build a complete decisioning pipeline step by step using a sample retail rewards dataset.

Get Started

Tutorials

Use Cases

Decisioning

Studio

Data Pipelines

AI & ML

Operations & Reporting

Governance & Security

Integrations

Reference

The Problem KaireonAI Solves

See It in Action: A Retail Rewards Program

Key Capabilities

Real-Time Decisioning

PRIE Ranking

9 Scoring Engines

Omnichannel Delivery

Visual Pipeline Editor

Built-In Experimentation

80+ Data Connectors

Five Monitoring Dashboards

LLM Explanations

Governed AI Layer

Platform Architecture

Architecture Deep Dive

Start Here

Try the Playground

Quickstart

Retail Rewards Tutorial

​The Problem KaireonAI Solves

​See It in Action: A Retail Rewards Program

​Key Capabilities

Real-Time Decisioning

PRIE Ranking

9 Scoring Engines

Omnichannel Delivery

Visual Pipeline Editor

Built-In Experimentation

80+ Data Connectors

Five Monitoring Dashboards

LLM Explanations

Governed AI Layer

​Platform Architecture

Architecture Deep Dive

​Start Here

Try the Playground

Quickstart

Retail Rewards Tutorial

The Problem KaireonAI Solves

See It in Action: A Retail Rewards Program

Key Capabilities

Platform Architecture

Start Here