
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