Automating a Surprise-Trip Engine to Cut Manual Work by 75%.
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The Challenge
Blookery's surprise-trip booking engine relied heavily on manual email processing and data extraction. Every booking required human intervention to parse customer emails, extract trip preferences, and match them with available packages.
The team was drowning in operational overhead. As booking volume grew, the manual workflow became the bottleneck — causing delays, errors, and an inability to scale without linearly increasing headcount.
The Founder-Led Solution
Applying my ‘Zero-Delegation’ engineering philosophy, I designed and built an automation engine that transformed Blookery's manual workflow into a streamlined, semi-automated pipeline.
- Built intelligent email parsing that automatically extracts trip preferences, dates, and budget constraints from customer communications.
- Created a matching algorithm that pairs extracted preferences with available trip packages based on weighted scoring.
- Implemented a dashboard for the operations team to review, approve, and override automated suggestions in one click.
Concrete Business Impact
The automation engine freed Blookery's team to focus on curating better experiences instead of processing emails.
Reduction in manual processing workload for the operations team.
Faster booking turnaround from customer email to confirmed trip.
Trips processed through the automated pipeline since launch.
“Manish understood our pain point immediately and built a system that gave us our time back. What used to take hours now takes minutes. He turned our biggest operational bottleneck into a competitive advantage.”

Jannis
Blookery
How We Built It
Workflow Analysis & Data Mapping
Mapped the entire manual booking flow end-to-end, identifying automation opportunities and data extraction patterns from customer emails.
Automation Engine Development
Built the email parsing engine, matching algorithm, and operations dashboard as an integrated but modular system.
Testing & Staged Rollout
Validated automation accuracy against historical bookings, then rolled out progressively to ensure reliability at scale.
Technology Stack
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