Accurately Qualify Marketing Leads with a Custom AI System

Most small sales teams start with their CRM's built-in lead scoring, like the system in HubSpot. This tool lets you add or subtract points for actions like opening an email or visiting the pricing page. The problem is that the logic is static. The system cannot learn that a demo request from a target-industry CTO is 50 times more valuable than an ebook download by an intern, so both might receive 10 points.

In practice, this creates noise. Consider a 10-person sales team getting 400 marketing leads a month. Their HubSpot rules give 5 points for any form fill. An ideal-fit prospect who fills out a high-intent 'Contact Sales' form gets the same score as a non-fit student downloading a whitepaper. Sales reps waste the first half of every day manually sifting through a list of equally-scored leads, trying to guess which ones are actually worth calling. High-value leads go cold while reps chase low-value contacts.

Upgrading to a tool like Salesforce Einstein seems like the answer, but it creates new problems for a small team. First, it requires an expensive Enterprise-tier license and at least 1,000 historical leads with defined outcomes before the model can even be trained. Second, the model is a black box. A rep sees a lead scored at '82' but has no idea why, making it impossible to tailor their opening line. The rep is still guessing, just with a more expensive tool.

The structural issue is that off-the-shelf platforms are built for generic business logic. Their data models are fixed and cannot incorporate the unique signals that drive your business, such as product usage data from your Supabase database or specific keywords from Google Ads that convert well. You need a system built around your specific conversion patterns, not a generic system your team has to work around.

The first step would be a data audit. Syntora would connect to your CRM and other key data sources to analyze your last 12-24 months of sales history. This process identifies which data points (like lead source, job title, pages viewed) are most predictive of a won deal and uncovers any data quality issues. You would receive a brief report on data viability and the potential accuracy of a model before any build work starts.

The technical approach would use a gradient boosting model built with Python libraries like Scikit-learn and LightGBM. This model would be wrapped in a FastAPI service and deployed on AWS Lambda for efficient, pay-per-use processing. When a new lead enters your CRM, a webhook would trigger the function. The service fetches the lead's data, generates a score and explanations using SHAP, and writes the results back to custom fields in your CRM. The entire process typically takes under 500 milliseconds.

The delivered system is a production-grade asset that you own completely. Your sales team sees the scores and explanations directly inside their existing CRM, requiring no new software to learn. You receive the full source code in your GitHub account, a runbook for maintenance and retraining, and a simple dashboard built on Vercel to monitor model performance.