Digitat
Marketing•Agentic AI•2025

AutoFlow AI

Agentic AI workflow automating lead research, email drafting, and outreach campaign triggering for sales teams with autonomous decision-making and human-in-the-loop controls.

AutoFlow AI

Client

GrowthForge

Role

AI Engineer

Timeline

8 weeks

Team

2 developers

Overview

GrowthForge's sales team was spending 30+ hours/week on manual lead research and email drafting. AutoFlow uses autonomous AI agents to identify qualified leads, research companies, draft personalized emails, and trigger campaigns—enabling 3x productivity.

Process

Built modular AI agents using LangChain with specialized roles: Lead Researcher, Email Drafter, Decision Agent. Connected agents through n8n for workflow orchestration. Implemented guardrails and human approval loops.

Key Features

✓Autonomous lead research agent with web search capability
✓Prospect company research and analysis
✓Personalized email generation based on research
✓Decision agent for lead scoring and prioritization
✓Campaign trigger automation through email systems
✓Human-in-the-loop approval for outreach
✓A/B testing framework for email variations
✓Response tracking and follow-up automation
✓Analytics dashboard with campaign performance metrics
✓Integration with CRM systems (Salesforce, HubSpot)

Challenges & Solutions

Built email templates with placeholders, implemented multi-stage prompts with refinement, added validation rules, and used feedback loop to improve outputs. Email usability improved to 78%.

Created standardized decision frameworks with explicit rules, added decision logging for auditing, implemented confidence scoring, and created human review for borderline cases. Consistency improved to 94%.

Integrated with CRM to pull interaction history, built memory system with vector database for prospect context, and added interaction checks before outreach. Duplicate outreach eliminated.

Implemented strict approval workflows for outreach, created admin dashboard for monitoring agent decisions, added audit trails, and enabled pause/restart controls. Full transparency achieved.

Results

Outreach Efficiency

30 hours/week→10 hours/week

3x

Email Usability

18%→78%

minimal edits

Research Time

8 hours→45 min

per prospect

Outreach Volume

50/week→180/week

per rep

Response Rate

4.2%→7.8%

personalization

Pipeline Impact

0→$1.2M

attributed

Goals

  • •Automate lead research and email drafting
  • •Maintain high quality of outreach messaging
  • •Enable sales team to reach 3x more prospects
  • •Keep human control and approval in the loop

Tech Stack

  • •Python
  • •LangChain
  • •OpenAI
  • •n8n

Target Users

  • •Sales development reps (SDRs)
  • •Account executives
  • •Sales managers

Key Learnings

  • •Agent chaining requires explicit handoffs and context passing
  • •Humans need visibility into AI decisions for trust and oversight
  • •Template-based generation with refinement beats pure generative output
  • •Feedback loops and continuous improvement are essential for agent quality

Future Plans

  • •Add voice-based outreach with AI calling
  • •Implement deal stage prediction for optimal timing
  • •Build multi-channel outreach (LinkedIn, phone, email)
  • •Add competitive intelligence gathering to research