Qualify Your Inbound Leads Faster with AI

Many marketing teams start with the built-in scoring in their CRM, like HubSpot or Marketo. These tools let you add points for actions like opening an email or visiting the pricing page. The problem is that these rules are static; they cannot learn that a lead from a partner referral is 10 times more likely to close than a lead from a webinar, even if they have the same point score.

A common scenario is a 15-person marketing team at a B2B company. Their sales reps waste hours each day chasing leads who requested a demo but have no budget, while high-intent leads who downloaded a whitepaper go cold. The team tries to fix this by adding more and more rules, creating a complex system of over 50 conditions that nobody on the sales team trusts or understands. The result is that reps ignore the scores and revert to manually triaging every lead.

More advanced platforms like Salesforce Einstein offer machine learning, but it's a black box. The system provides a score but not the reasons behind it, leaving reps unable to tailor their outreach. It also requires at least 1,000 historical leads to even begin training, a threshold many SMBs haven't met. The underlying issue is that these are features within a larger platform, architected for generality. They cannot incorporate unique signals from your business, like product usage data from a custom database, because their data models are fixed.

The engagement would start with a data audit. Syntora would connect to your CRM, marketing automation platform, and web analytics using their APIs to pull the last 12-18 months of data. The audit identifies the most predictive signals in your historical data and flags quality issues, like inconsistent deal stages or missing data. You would receive a report detailing the viable data features and a clear plan for the build.

The technical approach uses a gradient boosting model built with a Python library like LightGBM, as it excels at finding complex patterns in business data. This model would be wrapped in a FastAPI service and deployed on AWS Lambda for efficient, low-cost operation. When a new activity is logged in your CRM, a webhook would call the API, which returns a 0-100 score and the top 3 reasons for that score in under 300ms. Pydantic schemas would enforce data consistency between your systems and the model.

The delivered system integrates directly into a custom field in your existing CRM. Your sales team sees an 'AI Lead Score' and 'Scoring Reasons' without needing to learn new software. You receive the complete Python source code in your own GitHub repository, a detailed runbook for maintenance, and a simple dashboard to monitor model performance over time.