Or Rosen

AI Adoption Specialist turning skeptics to power users

Led over 150 SMB clients to adopt LLM-powered call analysis


are the clients struggling to deploy our system?

If you're asking yourself this, you already know the answer. We know what the platform can do, but the clients still churn over adoption friction. I close that gap. Let's talk.


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what i do

  • Integrate AI systems from pitch to deployment

  • Automate painful workflows (n8n, python, SQL)

  • Turn deployments into renewals

for example...

call center optimization

Upsold a medium-sized realty agency (~25 seats) to an AI-enabled tier, then implemented client-side solutions.
pain point: call review targets not met. management unaware of production environment trends

  • Applied an AI pipeline for sales support and trend analysis

  • Client call review targets were exceeded 2 weeks past implementation

  • Converted a hesitant collaborator to a primary platform owner


travel insurance agency transformation

Client directly approached for an upgrade to AI-enabled tier; I supplied the technical backing to make sure the deal sticks.
pain point: policy sale calls contain a certain data structure in a freeform format; structured information extraction trivialized quote generation

  • Automated data entry pipelines by using a user provided input structure

  • Specified CRM-compatible output formats using JSON Schema

  • Reduced adoption friction by providing production results, highlighting customizability and self-ownership


software boutique pitch analytics

Client was upgraded from a legacy package to a new AI-enabled tier. From the start, I targeted adoption and user independence.
pain point: human-written call summaries are lossy; LLM-generated outputs reduced qualification friction for agents

  • Crafted LLM prompts to extract BANT parameters from pitch calls, reducing representative friction

  • Reached project hand off within 2 weeks

  • Tailored prompts using best practices for platform abilities