Network Funding Group: AI Lead Personalization System
Network Funding Group scales outbound sales with an AI-powered lead intelligence system that automates research, enrichment, and personalized messaging at volume.
The Challenge
Network Funding Group relied on manual research and personalization to support outbound sales and partnership outreach. Each lead required individual analysis: reviewing company websites, identifying relevant context, extracting positioning signals, and crafting tailored messaging.
While effective at low volume, this approach created a hard ceiling on scale. As lead volume increased, research quality became inconsistent, turnaround time slowed, and valuable team hours were consumed by repetitive work. Network Funding Group needed a way to maintain high-quality personalization while dramatically increasing throughput.
Our Approach
A custom lead intelligence and personalization system was designed and implemented that automated the entire research and enrichment process from intake to delivery. The objective was not basic automation, but context-aware decision automation: evaluating lead data, performing structured research, generating relevant insights, and outputting sales-ready personalization at scale without human intervention.
Implementation Phases
Analyzed existing outbound workflow and broke it into discrete research and decision steps. Identified how leads were qualified, what contextual signals mattered most, and how personalization varied by industry and company profile.
Created structured data model to support raw lead inputs, intermediate research outputs, and finalized personalization fields. Enabled both single-record execution and high-volume batch runs.
Built multi-layered research pipeline that extracts company context, positioning indicators, and relevant external signals in parallel. Information is normalized and evaluated before downstream use.
Implemented adaptive logic that generates tailored messaging components while filtering out leads that fail quality thresholds, ensuring consistent output quality.
Final results written back to lead database in clean, structured format. Built-in safeguards handle rate limits, retries, and queueing for stability during large batch runs.
System Architecture
Raw lead data, company URLs, industry signals
System processes leads individually or in bulk, maintaining stability under load while handling incomplete or inconsistent input data.