Enterprise AI Architecture · Project 03

Personalized Help Experiences

Hybrid ML + heuristics infrastructure serving contextually relevant help to millions of users in real time.

Millions

Users served with
personalized help daily

Real-time

Signal integration across
product surfaces

Privacy-first

Cross-product
signal architecture

The Challenge

Replacing Generic Help with Context-Aware Personalization

Adityagen.ai built a real-time personalization platform that surfaces contextually relevant help to millions of users daily — eliminating unnecessary support contacts at scale.

Help surfaces across the product ecosystem were serving generic content — the same articles to every user regardless of context, history, or current issue. This created unnecessary support contacts, agent overhead, and user frustration when the answer was already available but never surfaced at the right moment.

Building a personalization platform for help content presented distinct challenges: strict latency constraints on a real-time serving system, complex multi-round privacy approvals for cross-product signal access, and multi-system signal integration complexity across disparate product surfaces. High-traffic scaling requirements demanded rigorous capacity planning and load testing before every launch.

"Personalization at this scale isn't a model problem — it's an infrastructure problem. The model is the easy part."

System Architecture

Four-Component Personalization Platform

Component 01

Personalization Service

Java-based backend defining APIs, models, and core components. Real-time signal integration for live context-aware recommendations.

Component 02

ML Recommendation Infrastructure

Feature extraction pipelines feeding TensorFlow training workflows. Servo-based model serving for low-latency inference. Interaction logging integration for continuous model improvement.

Component 03

Signal Integration Layer

Cross-product signal API federation with authentication adaptations. Scalability optimization for high-traffic signal consumption.

Component 04

Personalized Content Service

Rule config system enabling configurable targeting logic without model retraining. Third-party API integration with SLO-compliant infrastructure.

Key Innovations

Hybrid ML + rule-based architecture combining model intelligence with configurable business logic

Cross-product signal federation enabling help surfaces to draw context from across the full product ecosystem

Privacy-compliant architecture meeting regulatory and policy requirements without sacrificing targeting granularity

Outcomes & Impact

Better Help, Fewer Contacts, Lasting Foundation

Millions

Users served with
relevant help daily

↓ Contacts

Fewer unnecessary
agent escalations

∞ Scale

Compounding value
as signal library grew

The platform improved user satisfaction by surfacing more relevant help content at the right moment in the user journey. Reduced support costs followed directly from fewer unnecessary contacts reaching live agents.

The long-term personalization foundation established by this project enabled scalable content targeting across channels, creating compounding value as the signal library and model quality improved over time.

My Role

Privacy, Architecture, and Engineering Leadership

Drove the privacy design process, navigating regulatory and policy requirements to unblock cross-product signals
Co-authored key architecture documents defining the personalization platform's long-term design
Mentored junior engineers through build, testing, and production launch phases

Tech Stack