Reconstructed for Portfolio — NDA Protected
Built an AI-powered onboarding system that reduced manual work by over 80% and scaled client capacity by 10x, enabling the team to focus on strategy and growth.
Context
A growing B2B services organization faced operational bottlenecks due to a manual and error-prone client onboarding process. The team was spending hours each week performing repetitive tasks like data entry, verification, and email communication, limiting scalability and client satisfaction.
My Role & Responsibilities
Designed the automated onboarding architecture end-to-end.
Integrated AI and workflow automation tools to handle data collection and validation.
Collaborated with the operations and marketing teams to train staff and ensure adoption.
Conducted testing, optimization, and performance monitoring post-launch.
Tech Stack
Conversational Interface Platform
OpenAI API for generative logic
Workflow automation via Zapier
HubSpot CRM integration
Steps I Took
Discovery & Workflow Mapping – Analyzed existing onboarding steps to identify manual bottlenecks.
Conversational Flow Design – Replaced web forms with an AI-driven chat that provided a natural client experience.
Real-Time Data Validation – Implemented automated checks for company legitimacy, contact info accuracy, and data completeness.
Personalized Communication – Developed prompt-based automation for customized onboarding emails reflecting client context.
Seamless CRM Automation – Synced all validated data and workflows into the CRM with tagging and task logic.
Iterative Testing – Piloted with a small client subset, refined scripts, and optimized based on user feedback.
The worst part? They were actually turning away potential clients because they couldn't handle the onboarding workload. That's when I knew I had to build something special.
Outcome (Approximate / Relative Metrics)
Time Savings: Reduced onboarding per client from ~2.5 hours to ~20 minutes.
Efficiency Gain: 85% reduction in repetitive admin tasks.
Scalability: Increased weekly onboarding capacity nearly 10x.
Data Accuracy: Improved data integrity to near-perfect reliability.
Team Morale: Significantly improved employee satisfaction through workload relief.
Learnings & Trade-offs
Starting small helped prove ROI quickly, but more robust documentation from day one could have accelerated scaling.
Prioritizing human tone and fallback logic in automation built greater user trust.
Balancing AI autonomy with human oversight was key to sustainable adoption.
