Build a Custom AI Recommendation Engine for Your Marketing

Most small marketing teams start with HubSpot Workflows or Mailchimp Journeys. These tools are great for basic, rule-based segmentation. For example, if a contact's property is 'Lead,' send them the 'Nurture' email sequence. But this is a static system. It cannot learn which content actually leads to conversion for a specific user segment or adapt when user behavior changes. You end up with dozens of brittle, hard-to-maintain workflows that treat people like entries in a database.

Consider a 15-person B2B SaaS company using HubSpot. A prospect downloads an ebook on 'lead scoring' and is placed in a workflow with five pre-written emails on that topic. But a review of their activity shows they also spent 10 minutes on a technical blog post about API integrations. The rigid HubSpot workflow misses this crucial buying signal. It continues sending generic marketing content instead of pivoting to the technical information the prospect is actually interested in. The only way to catch this is for a marketing manager to manually review contact activity logs, which is impossible to do for hundreds of leads.

WordPress plugins like OptinMonster offer another form of personalization, but it is surface-level. These tools use simple triggers like exit-intent or referral source to show a targeted popup. They cannot dynamically change the content *on the page* based on a user's entire history with your brand. The core architectural problem is that these platforms are designed for one-to-many communication with coarse segmentation. They are not learning systems capable of calculating the semantic similarity between your 150 blog posts and a user's unique interest profile. To do that, you need a system built for that specific purpose.

The engagement begins with a content and data audit. Syntora connects to your Google Analytics, CMS, and CRM to map all existing content assets against user interaction data. This audit identifies what content drives the most engagement and which user behaviors correlate with conversion. You receive a report that outlines the available data signals, identifies any gaps, and proposes a specific model for predicting which content a user should see next.

The technical approach uses a FastAPI service to ingest user events and serve recommendations. To understand your content, the system uses the Claude API to generate vector embeddings for each article, case study, and landing page. This allows for finding semantically related content, not just simple keyword matches. A user's interaction history is stored in a Supabase database, and a lightweight model written in Python with scikit-learn predicts the next best content. This service is deployed on AWS Lambda for efficient, on-demand processing that responds in under 300ms.

The final deliverable is a simple API endpoint your website can call. When a user arrives, the API returns a ranked list of personalized content IDs that you can render on your site, in emails, or in your app. You receive the complete Python source code, a dashboard to monitor recommendation performance, and a runbook detailing how to retrain the model as you add new content. The system integrates into your current workflow, not the other way around.