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The Decision Lifecycle

Every decision KaireonAI makes follows the same lifecycle. Whether you are personalizing a website in real time or generating a batch of campaign recommendations, the steps are identical — only the trigger changes. Think of it as an assembly line: a customer event enters one end, passes through a series of stages, and a personalized recommendation comes out the other side. After delivery, the customer’s response feeds back in so the system gets smarter over time.

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Building Blocks

KaireonAI has 10 core building blocks organized in three layers.

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Data Foundation

Connect KaireonAI to your external data — databases, cloud storage, streaming platforms, CRMs, and APIs. There are 22 connector types out of the box. You configure a connector once (credentials, endpoint, format) and then reuse it across as many pipelines as you need.
Visual ETL workflows built with a drag-and-drop editor. Each pipeline is a graph of source → transform → target nodes. There are 14 built-in transforms including rename, filter, cast, deduplicate, join, and PII masking. Pipelines move data from your external systems into KaireonAI schemas.
Entity definitions for things like customers, accounts, and transactions. When you create a schema, KaireonAI creates a real PostgreSQL table behind the scenes. You load data in through pipelines, and decision flows read data out at decision time through the enrichment stage.

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Decisioning Logic

A two-level business hierarchy that organizes your offers. For example, “Credit Cards” might be a category with sub-categories “Travel Rewards” and “Cash Back”. Categories also define custom fields — including computed fields with formulas that calculate personalized values at decision time.
The core unit of KaireonAI. An offer is anything you want to recommend to a customer: a product, a promotion, a message, an action. Each offer has a priority, an optional budget, and scheduling rules. Every offer belongs to a category.
Rules that determine who is eligible for an offer. Hard rules are pass/fail gates (for example, age >= 18). Soft rules act as score multipliers — a loyalty-member rule might apply a 1.2x boost instead of disqualifying non-members outright.
Safeguards that prevent over-contacting customers. You can set frequency caps, cooldown periods, budget limits, and mutual-exclusion rules. Policies can apply globally, per offer, per channel, or per creative.
The heart of KaireonAI. A decision flow is a visual pipeline with five stages:
  1. Enrich — Load customer data from schema tables
  2. Compute — Evaluate formulas to produce personalized values
  3. Filter — Apply qualification rules and contact policies
  4. Score — Run machine-learning models or scorecards
  5. Rank — Arbitrate across competing offers using multi-objective scoring
You build decision flows in a drag-and-drop editor, and the platform executes them either in real time (via the Recommend API) or in batch (via Runs).

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Delivery & Feedback

Where offers get delivered. KaireonAI supports email, push notifications, SMS, in-app messages, web banners, webhooks, WhatsApp, and direct mail. Each channel has a delivery mode — API-driven, file-based, or manual.
Content variants for an offer on a specific channel. A single offer might have a short-copy email creative, a rich-HTML email creative, and a push notification creative. Creatives support personalization variables and A/B test variants.
The vocabulary of customer responses. Common outcomes include impressions, clicks, conversions, and dismissals. You record outcomes through the Respond API, and KaireonAI uses them to measure performance and close the learning loop.
Aggregated interaction signals that KaireonAI calculates over time — conversion rate over the last 30 days, revenue per customer, click-through rate by channel. Behavioral metrics feed back into qualification rules and scoring models so that decisions improve automatically.

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How They Fit Together

The three layers connect through data flow and feedback: Reading the diagram:
  • Left to center: Pipelines load external data into schemas. At decision time, the Enrich stage in a decision flow reads from those same schemas to hydrate customer context.
  • Center: Offers, qualification rules, and contact policies feed into a decision flow. The flow scores and ranks candidates to produce a recommendation.
  • Center to right: Winning offers are delivered through channels as creatives. Customer responses are recorded as outcome types.
  • Right back to center: Behavioral metrics — built from recorded outcomes — feed back into rules and scoring models, closing the learning loop.

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Three Ways to Use KaireonAI

Real-Time API

Call the Recommend endpoint and get ranked, personalized offers back in milliseconds. Record customer responses with Respond. Best for web personalization, in-app recommendations, and call-center prompts.

Batch Runs

Execute a decision flow against an entire customer segment in one go. Produces a per-customer recommendation list you can export or push to a downstream system. Best for email campaigns, daily offer refreshes, and regulatory reporting.

Journeys

Multi-step orchestrated workflows that combine decisions with wait steps, branches, and sends. A journey might trigger a recommendation, wait two days, check for a response, and branch accordingly. Best for onboarding sequences, lifecycle campaigns, and retention programs.

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Next Steps

Quickstart

Get KaireonAI running locally in under 5 minutes.

Platform Walkthrough

Build a complete decisioning setup from scratch, step by step.