Predict High-Intent Leads with Custom AI Automation

Many SMB marketing teams start with HubSpot's native lead scoring. The tool lets you assign points for actions like form fills or email opens, but it's a static, rule-based system. It cannot learn from your historical sales data. A lead who downloaded three whitepapers gets a high score, while a lead who visited the pricing page twice gets a low one, even if the latter behavior is a far stronger buying signal.

Consider a 10-person marketing team at a B2B SaaS company. Their best leads come from users who sign up for a free trial AND visit the API documentation page. A rule-based system can award points for the trial signup but often fails to properly weigh the combination of activities. A salesperson wastes hours calling a low-intent lead with a high point score while missing the high-intent lead who is actively evaluating the product's technical fit.

More advanced platforms promise AI scoring, but it's often a black box. The system might require 1,000+ conversions to even activate, a threshold many SMBs can't meet for months. When it does work, it provides a score like '87' with no explanation. A sales rep cannot trust a number they don't understand, so they revert to manual triage. The expensive AI feature goes unused because it provides no actionable context.

The structural problem is that these off-the-shelf tools are built for a generic customer profile. Their data models are fixed. They cannot incorporate your company's unique buying signals, like specific feature usage in your app's Supabase backend. You are forced to adapt your process to their software, instead of building software that models your actual customer journey.

The engagement would begin with a data audit. Syntora would connect to your CRM, website analytics, and any product databases to map out all potential lead signals, aiming to identify at least 50 candidate features. This audit produces a data readiness report that confirms you have enough signal for a predictive model and outlines any required data cleanup before the build begins.

The technical approach would use a Python model wrapped in a FastAPI service and deployed on AWS Lambda for serverless execution. When a new lead appears in your CRM, a webhook triggers the function. The service processes the lead's data, generates a score in under 200ms, and writes it back to a custom CRM field. The system would also generate explanations, so your sales team sees why a lead scored high, building trust and informing their outreach.

The delivered system lives in your own cloud account. Your team sees the scores natively in their existing CRM without learning a new tool. You receive the full source code in your GitHub, a runbook for maintenance, and a simple monitoring dashboard built on Vercel to track model accuracy over time. Total hosting costs are typically under $20 per month.