/ Sales and marketing operations · PUBLISHED CASE
Apprely
Prospecting and outreach organised into a more repeatable system
An IT services business introduced AI-supported prospecting and outreach, reducing repetitive business-development work.
/ THE SHORT VERSION
An IT services business introduced AI-supported prospecting and outreach, reducing repetitive business-development work.
01 / BUSINESS CONTEXT
The work before the tool.
Apprely’s marketing team researched target companies, found decision-makers, assembled lists and managed outreach across several platforms and projects.
02 / THE INTERVENTION
What changed in the workflow.
The business integrated Apollo, Salesforce and LinkedIn Sales Navigator into its workflow for targeting, lead generation, outreach and CRM management. It also began evaluating n8n for broader agent-based automation.
AI-supported sales tools were in use. The publication treats end-to-end agent automation as future work, so it is not presented here as deployed.
03 / REPORTED OUTCOMES
The numbers, with their labels attached.
| Workflow area | Before | After |
|---|---|---|
| Prospecting | Targets and decision-makers researched manually | AI-supported platforms assist targeting and list building |
| Outreach | Campaign steps repeated across projects | A more structured toolchain supports recurring outreach |
| Automation scope | Manual work across the funnel | Current tools are live, while full agent automation remains under evaluation |
Capability matters when people stay in control.
People remained accountable for market selection, messaging, qualification and customer relationships. Proposed end-to-end agents were still under evaluation.
05 / WHAT A SIMILAR BUSINESS SHOULD TEST
Transfer the pattern, not the conclusion.
- 01
Automating outreach volume is not the same as improving qualified pipeline.
- 02
Approved positioning and customer language should come before message generation.
- 03
Separate current capability from the automation roadmap.
06 / SOURCE AND LIMITS
What this page can and cannot establish.
This analysis summarises outcomes published in MCCIA’s AI: Compilation of case studies (2026), printed pages 48–49. The work was conducted through the MCCIA Applied AI Studio. The report credits the Studio team managed by Neeraj Thakur, with Gauri Kale and Ismail Patel. The featured organisation is not presented as a dotSuper client.
- 01The report does not publish reply, qualification, opportunity or revenue conversion rates.
- 02The manual-effort reduction is self-reported and may combine several sales platforms and process changes.
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