Case Studies/Lead Generation Automation
Insurance · Sales Automation · Lead Intelligence

340% More Qualified Leads.
In 90 Days.

A 12-person commercial insurance sales team was spending 60% of their day on manual research, data entry, and unstructured prospecting. Quixas replaced the entire workflow with a multi-agent lead intelligence system: automated enrichment, AI scoring, and personalised outreach sequences. 90 days later: 340% more qualified leads, $420K in new pipeline, and 32 hours per week returned to selling.

Industry

Commercial Insurance

Team Size

12-person sales team

Delivered

6 weeks, ROI in 45 days

New Pipeline Added, Q1

$420K

Weighted pipeline in first quarter

Sourced entirely through the automated system, zero manual prospecting involved.

340%

More qualified leads

32hrs

Saved per week

68%

Better conversion

Live in production

0%

More qualified leads

~45/mo to 198/mo in 90 days

$420K

Pipeline added in Q1

Automated system only

0%

Better lead-to-meeting

vs previous cold outreach

32hrs

Saved per week

Across the sales team

The Challenge

A Sales Team Spending
60% of Their Day Not Selling.

Sales reps spending 5-6 hours per day on research and data entry

A 12-person team managing 800+ active accounts, but new business was stalling. Reps were manually researching prospects, copy-pasting into HubSpot, and building outreach sequences from scratch. 60% of their day on work that should not require them.

14,000 CRM contacts with only 9% enrichment coverage

HubSpot had 14,000+ contacts, but fewer than 9% had been enriched with actionable firmographic or intent data. The vast majority of the database was effectively unusable for targeted outreach.

Zero visibility into which accounts were actively in-market

No way to identify which of their 14,000 contacts were experiencing business changes (mergers, expansions, compliance events) that signalled active insurance purchasing intent. Every rep was prospecting blind.

Inconsistent outreach with no logic behind the sequencing

Some leads received one email, others received eight, with no systematic logic behind the cadence or personalisation. There was no connection between what a prospect's profile indicated and what message they received.

What We Built

A Multi-Agent
Lead Intelligence System.

Quixas designed and deployed a modular multi-agent architecture connecting data enrichment, AI scoring, and intelligent outreach orchestration into a unified system. Built in 6 weeks, ROI delivered in 45 days.

Automated Data Ingestion

Automated scraping pipelines pull prospect data from Apollo, LinkedIn profiles, state licensing databases, and industry directories. n8n workflows run on a weekly cadence, deduplicating against the existing CRM and flagging net-new leads for enrichment. Backfilled 6,200 existing contacts in week two.

RAG-Powered Enrichment Agent

A RAG-powered agent enriches each lead with firmographic data (revenue, employee count, NAICS codes), technographic signals (current insurance platforms), and contextual insights from company websites and news mentions. GPT-4 with a custom insurance vertical knowledge base.

Multi-Agent Lead Scoring

Built on LangGraph with human-in-the-loop checkpoints. Each lead scored across four dimensions: firmographic fit, behavioral intent signals, recency of business changes (mergers, expansions, compliance events), and engagement history. Model retrains monthly on closed-won deal attributes.

Intelligent Outreach Engine

High-scoring leads routed into personalised outreach sequences via n8n automations synced to HubSpot. Each sequence dynamically assembled based on the lead's enrichment profile: industry-specific pain points, recent triggers, preferred channel. Follow-up cadences adapt based on engagement signals.

Real-Time Pipeline Dashboard

Live pipeline visibility with high-intent alerts, flagging accounts showing active buying signals before competitors identify them. Management sees qualified lead volume, conversion rates, and sequence performance without manual reporting.

Closed-Loop Feedback Learning

Every meeting booked, quote sent, and deal closed feeds back into the scoring model. The system learns which signals actually convert for this team, in this vertical, in this market, and recalibrates monthly. Lead quality compounds over time instead of decaying.

How the System Works

Data Ingestion

Apollo + Web Scraping

Enrichment Agent

GPT-4 + RAG

Lead Scoring

LangGraph Multi-Agent

Outreach Engine

n8n + HubSpot

Pipeline Dashboard

Real-Time Alerts

Data SourcesApollo.io, LinkedIn, state licensing databases, industry directories
EnrichmentGPT-4 + RAG (Pinecone vector store)
ScoringLangGraph, multi-agent, human-in-the-loop
Outreachn8n + HubSpot API
DatabaseSupabase
DashboardReact, real-time pipeline analytics
Delivery Timeline

6 Weeks from Kickoff to Production.

Week 1-2

Discovery & Data Audit

CRM data quality assessment, sales workflow mapping, ICP definition workshop, and architecture design. Identified 14,000+ contacts with only 9% enrichment coverage.

Week 2-3

Ingestion & Enrichment Build

Deployed data ingestion pipelines across 4 sources, built RAG-powered enrichment agent with Pinecone vector store, and backfilled 6,200 existing contacts with firmographic data.

Week 3-5

Scoring Engine & Outreach Automation

Built LangGraph multi-agent scoring pipeline, trained initial model on 18 months of closed-won data, and configured dynamic outreach sequences in n8n connected to HubSpot workflows.

Week 5-6

Dashboard, Testing & Handoff

Deployed real-time pipeline dashboard with high-intent alerts, conducted parallel testing with the sales team, documented SOPs, and provided team training on system management.

The Results

90 Days in Production.
$420K in New Pipeline.

340%

Increase in qualified leads

Monthly qualified leads entering the pipeline grew from ~45 to 198. AI scoring filters out low-fit prospects before they consume rep time.

32 hrs

Saved per week across the team

Sales reps reclaimed 5+ hours daily previously spent on manual research, data entry, and unstructured prospecting across spreadsheets.

68%

Better lead-to-meeting conversion

Personalised, trigger-based outreach sequences converted 3.4x better than the previous spray-and-pray approach to cold outreach.

$420K

Pipeline added in Q1

New qualified opportunities sourced entirely through the automated system, within the first quarter of deployment.

We went from our reps spending half their day Googling companies and copy-pasting into HubSpot, to having a system that delivers scored, enriched leads with personalised outreach already queued up. The Quixas team understood our workflow deeply and built something that actually fits how our team sells.
V

VP of Sales

Regional Commercial Insurance Brokerage, Southeast US

Client name withheld by request.

For Sales Leaders & RevOps

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