# How Mindflow achieved a 35% reply rate using intent-based outbound

**How Mindflow achieved a 35% reply rate using intent-based outbound with Gojiberry AI**  
Nathan Amram - Growth Automation Manager

**+31%**  
Reply rate

**21%**  
of replies converted into opportunities

**Overview**  
[Mindflow](https://mindflow.io/) is an AI automation platform helping enterprise teams orchestrate workflows across thousands of APIs.  
Their customers include large organizations such as Auchan, and their sales process targets cybersecurity leaders like **CISOs and SecOps teams**.  
But generating qualified pipeline for enterprise cybersecurity software is difficult.  
Traditional outbound methods simply weren’t enough.

**The challenge**  
Before using Gojiberry, Mindflow relied on **traditional outbound built on ICP lists**.  
Their approach was typical:  
- build prospect lists  
- send cold outreach campaigns  
- manually personalize messages

But this created a fundamental problem.  
Most prospects **had no buying intent**.  
Nathan Amram, Growth Automation Manager at Mindflow, explains:  
_“Cold outbound felt too cold. We were reaching out to people without knowing if they were actually interested.”_

To improve results, the team experimented with technical workarounds:  
- tracking followers of competitors  
- monitoring engagement on cybersecurity influencers  
- searching LinkedIn manually for niche keywords

But these signals were **difficult to monitor and impossible to scale**.  
Mindflow needed a way to **systematically detect buying signals**.

**The solution**  
Mindflow integrated **Gojiberry’s intent data directly into their internal automation agents**.  
Instead of relying on manual prospecting, the company built an **automated intent-driven pipeline**.  
The system runs daily and automatically identifies new prospects showing relevant signals.

**Signals tracked**  
Mindflow monitors several types of high-intent signals:  
• engagement on competitor posts  
• reactions to cybersecurity influencers  
• posts mentioning niche keywords such as  
  – SOAR  
  – security automation  
  – workflow automation  
  – agentic AI

The system also detects explicit pain signals such as discussions around **“alert fatigue.”**  
These signals indicate that a prospect may already be exploring automation solutions.

**The workflow**  
Mindflow connected the Gojiberry API to its internal growth automation system.  
The pipeline operates in three steps.

**1 — Detect intent signals**  
Every day, Mindflow’s internal agent pulls fresh leads directly from the Gojiberry API.  
These leads already contain **intent signals indicating potential interest**.

**2 — Filter by ICP**  
The automation system filters leads based on:  
- company type  
- job titles (CISO, SecOps, DataOps)  
- geography  
- company size

Only **high-quality matches** enter the pipeline.

**3 — Generate personalized outreach**  
A second agent then:  
- scrapes LinkedIn profiles  
- generates personalized outreach sequences  
- launches campaigns

Each sequence is automatically adapted based on the **intent signal detected**.  
Nathan describes the system as:  
“A fully automated engine that feeds our outbound with fresh, intent-driven leads every day.”

**Results**  
Using Gojiberry signals, Mindflow dramatically improved the performance of their outbound campaigns.

**Campaign performance**  
- **31–35% reply rate**  
- **21% of replies convert into MQLs**

Even with deliverability limitations restricting sending volume, the system consistently generates qualified conversations.

**Business impact**  
Mindflow sells enterprise software with:  
- **~€25,000 average contract value**  
- **~18-month sales cycle**

This means that **a single deal generated from intent signals can pay for Gojiberry for years.**

**Why intent-based outbound works**  
For Mindflow, the difference is simple.  
Instead of contacting random ICP lists, they reach out to prospects **already discussing relevant topics**.  
Examples include:  
- security teams discussing automation challenges  
- engineers complaining about alert fatigue  
- professionals engaging with competitor content

This allows the team to start conversations **at the moment interest already exists**.  
Nathan summarizes the shift clearly:  
“Intent signals change outbound completely. You’re no longer guessing who might be interested.”

**Product experience**  
For Mindflow, reliability was essential.  
Nathan highlights two key strengths of Gojiberry:  
- a robust API  
- clear documentation

Once the integration was completed, the system required very little manual intervention.  
“The API and documentation are excellent. Once everything was connected, it just worked.”

**What’s next**  
Mindflow plans to continue expanding their intent-driven acquisition model.  
Future initiatives include:  
- tracking additional cybersecurity signals  
- improving signal scoring  
- scaling their automated growth agents

Their long-term vision is to build a **fully autonomous pipeline generation system powered by AI.**

**Final takeaway**  
Enterprise sales teams often struggle with outbound efficiency.  
Mindflow solved this by combining **AI automation agents with Gojiberry’s intent data**.  
The result is a system that continuously identifies high-potential prospects and feeds their outbound engine with qualified leads.  
As Nathan concludes:  
_“Intent-based lead generation is the future of outbound.”_