Fordel StudiosLead Qualification Bot
Score inbound leads on intent signals and fit criteria — route the best ones before they go cold.
The problem
being solved
A B2B SaaS company generating 500+ inbound leads monthly uses static lead scoring: job title gets 10 points, company size 15 points, whitepaper download 20 points. This treats all VP titles equally, ignores buying committee dynamics, and scores competitors the same as prospects.
6sense demonstrated that intent signals across buying committees identify in-market accounts. Drift proved conversational AI can engage visitors and book meetings in real time. Predictive scoring increases sales acceptance rates by up to 35% versus rules-based scoring (Forrester 2024).
The result of bad scoring: sales wastes time on unqualified leads, qualified prospects go cold waiting, and marketing cannot measure true lead quality.
How this
agent works
This agent replaces static scoring with a predictive model trained on your closed-won and closed-lost data. It evaluates leads across firmographic fit (company size, industry, tech stack), behavioral signals (pages viewed, content consumed, engagement frequency), and third-party intent signals where available.
When a high-scoring lead engages — visits pricing, returns for a third session, submits a demo request — the agent engages conversationally, asks qualifying questions aligned with your sales methodology (BANT, MEDDIC, or custom), captures budget and timeline, and books a meeting on the rep's calendar.
Sales receives a complete lead dossier: qualification answers, behavioral timeline, firmographic profile, and fit score with transparent feature explanations.
We train an XGBoost scoring model directly on your historical CRM data (Salesforce or HubSpot), using closed-won patterns to weight firmographic fit, engagement depth, and third-party intent signals. A FastAPI service handles real-time scoring at form submission, while a TypeScript/React qualification layer runs conversational follow-up via chat or email using GPT-4o with your sales methodology as the prompt scaffold. PostgreSQL stores lead dossiers and scoring history; Redis handles session state for multi-turn qualification flows. Setup takes 2–3 weeks: data audit, model training, integration wiring, and rep routing configuration.
- 01
CRM-Trained Scoring Model
XGBoost model trained on your actual closed-won and churned data, not generic SaaS benchmarks. Scores on ICP firmographic fit, product engagement depth, and intent signals from sources like G2 or Bombora. Each score ships with SHAP-based feature explanations so reps know why a lead ranked high.
- 02
Conversational Qualification
High-scoring leads get a follow-up sequence via chat widget or email — GPT-4o running questions mapped to your MEDDIC, BANT, or custom qualification framework. Responses are parsed and structured, not just logged. Low-quality answers drop the score and route the lead to nurture automatically.
- 03
Calendar-Aware Meeting Booking
Qualified leads book directly into the assigned rep's calendar based on territory rules and real-time availability via Google Calendar or Outlook. Confirmation emails include a prep summary pulled from the lead's dossier. No SDR intervention required for the top 20% of inbound.
- 04
Structured Lead Dossier
Before the meeting fires, the agent assembles a briefing doc: qualification answers, behavioral timeline (pages visited, content downloaded, pricing page hits), firmographic profile, and score breakdown. Delivered to the rep via Slack or CRM task — everything they need to open without asking basic questions.
Build this agent
for your workflow.
We custom-build each agent to fit your data, your rules, and your existing systems.
Free 30-min scoping call