Personalize Your Website Content With Custom AI

Marketing teams often start with tools like HubSpot's Smart Content or, previously, Google Optimize. These platforms are great for basic A/B testing or showing different content to a static list of contacts. They let you define simple rules, but they cannot interpret complex user behavior or learn from it.

Consider a B2B SaaS company that wants to show different messaging to enterprise versus SMB visitors. In HubSpot, this is easy if the visitor is already a contact on a list. But the most valuable signal is often behavioral, like a user visiting the API documentation three times in a week. HubSpot's rules are not designed to capture these nuanced, multi-session patterns. This forces marketers into creating dozens of hyper-specific lists that are impossible to maintain.

More advanced platforms like Optimizely offer personalization, but their engines depend on audiences you build manually. Connecting your own product database to show recommendations based on in-app usage often requires their highest enterprise tier and a separate services engagement. The architecture of these tools assumes segmentation happens before a visitor arrives. They are not built to make a complex decision in real time based on the visitor's first few clicks.

The structural problem is that these tools are designed for rule-building, not real-time inference. They provide a user interface for marketers to set conditions, but there is no place to insert your own data model. You cannot write a Python function that queries your own database and tells the platform what content to show. This walled-garden approach is what prevents you from moving beyond basic demographic personalization.

A project would begin with a data audit. Syntora connects to your Google Analytics, CRM, and any product databases to map out available data points. The goal is to identify the 10-20 signals that best predict a visitor's intent or segment, such as company size, industry, specific pages viewed, or time on site. You receive a data plan that specifies which features will be used to train the model.

Syntora would then build a lightweight classification model, wrapped in a FastAPI service, to score visitors against target segments. The model is deployed on AWS Lambda to ensure a low-latency response, typically under 50ms. This FastAPI endpoint receives visitor data from a script on your site, runs it through the model, and returns a simple key (e.g., 'headline_enterprise', 'cta_smb') that tells your website which content to display. This architecture decouples the AI logic from your website's codebase.

The delivered system is a single, secure API endpoint and a small JavaScript snippet for your website. Your marketing team can manage all the content variations, like headlines and images, in a simple Supabase table without needing to write any code. The system tracks impression and conversion rates for each variation, creating a feedback loop to refine the personalization strategy. You receive the full source code and a runbook for maintenance.