Customer Experience · Azure AI Foundry

Recommendations that convert. Powered by real-time signals.

Kovil AI builds Azure-native personalisation engines that ingest live behavioural signals, match them against semantically embedded product catalogues, and serve sub-100ms recommendations that drive measurable conversion and revenue lift.

+28%
Conversion rate
+19%
Average order value
3x
Engagement rate
Real-time
Personalisation

How It Works

From user signal to personalised recommendation in milliseconds.

01

User Signal Pipeline

We build an Azure Event Hubs ingestion pipeline that captures real-time behavioural signals from your web and mobile surfaces, enriching them with CRM history stored in Azure Cosmos DB.

  • Event Hubs capture clicks, views, add-to-cart, and purchase events at scale
  • Stream processing enriches events with CRM segment and LTV data
  • User profiles updated in Cosmos DB within seconds of each interaction
02

Semantic Similarity Engine

Azure OpenAI Embeddings encode your product catalogue and content library into a vector index in Azure AI Search. At query time, the user's live profile is embedded and matched against this index.

  • Product catalogue embedded nightly; real-time embedding for new listings
  • Hybrid vector + metadata filtering for category, price range, and availability
  • Re-ranking layer applies business rules (margin, inventory, promoted items)
03

Recommendation API Deployment

Results are served via a low-latency Azure API Management endpoint that your frontend queries. Kovil AI provides SDKs for web, iOS, and Android with built-in A/B test assignment.

  • P95 response time under 80ms at production load
  • Feature flags control recommendation strategy per user segment
  • Application Insights telemetry for click-through rate and revenue attribution

Capabilities

What this agent can do.

Real-Time Behavioural Signal Ingestion

Capture browsing events, cart additions, dwell time, and purchase history in real time via Azure Event Hubs, building a live user profile that updates with every interaction.

Semantic Product & Content Matching

Azure OpenAI Embeddings represent products and user intent in the same vector space, enabling semantic similarity matching that goes far beyond collaborative filtering.

CRM History Integration

Enrich real-time signals with long-term purchase history, lifetime value, and segment attributes from your CRM, ensuring recommendations are informed by the full customer relationship.

A/B Testing Framework

Built-in experiment management lets you test recommendation strategies, ranking models, and UI placements with statistical significance tracking and automatic winner selection.

Explainable Recommendations

Each recommendation includes a human-readable reason ('Because you viewed X' or 'Popular with customers like you'), increasing click-through and customer trust.

Personalisation at Any Scale

Azure Cosmos DB and Azure API Management deliver sub-100ms recommendation responses at any request volume, with horizontal scaling built in from day one.

Built With

Azure technology stack

Azure OpenAI EmbeddingsAzure AI Search (vector)Azure Cosmos DBAzure Event HubsSemantic KernelAzure API Management

Turn browsing intent into revenue, automatically.

Book a call to discuss your catalogue size, traffic volumes, and what a 28% conversion lift would mean for your business.