From Data Chaos to Self-Service
How I help Series A/B biotech companies remove data bottlenecks by building tools that empower scientists to access insights independently.
The Laptop That Couldn't Sleep: Scaling Plasmid Build QC
Series B Synthetic Biology Company
Plasmid QC workflow running on a single scientist's MacBook overnight, creating bottlenecks and single-person dependency
Overnight runs → 30-40 seconds per sample
Python
AWS Batch
Docker
BLAST
GitHub
Internal UI framework
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AI Readiness Starts with Knowing What You Have
Large Pharma Company
Multi-modal discovery data scattered across teams with no catalog, inconsistent metadata, and knowledge locked in individuals
90% reduction in data discovery time (30-60 min → 2-5 min)
Python
Streamlit
SQLite
Docker
YAML
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From 8 Hours to 45 Minutes: Democratizing High-Throughput Screening Data
Series B RNA Therapeutics Company
5-person HTS team spending 8-12 hours/week on manual data reporting (CSV → Prism → PowerPoint)
94% time reduction (8-12 hrs → 45 min per week)
Python
Streamlit
AWS (EC2, S3)
PostgreSQL
PositConnect
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