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 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.”