Case Study: How an Antibody Therapeutics Team Eliminated 20–30 Hours of Monthly ELN Tracking with Benchling Automation
Client | Antibody Therapeutics Company |
Service | Benchling ELN Automation Case Study | Karchem Consulting |
Key Results |
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A biotechnology company developing antibody-based therapeutics partnered with Karchem Consulting to automate electronic lab notebook (ELN) status tracking across a 15-scientist Benchling environment. Karchem built a Python automation that connects to Benchling through its API using read-only access, flagging entries that are overdue for review, rejected and awaiting resubmission, or stalled in progress, and sending individualized weekly reports to the relevant authors and reviewers. Working with the client's IT team, Karchem also resolved firewall, security, and deployment requirements to run the automation within the client's environment. The solution went live in under one week and eliminated 20–30 hours of manual tracking each month, all without ever modifying Benchling records.
Introduction
Our client is a biotech company developing antibody-based therapeutics, running its research and development on a single Benchling environment used by a team of 15 scientists. Their day-to-day work generates a steady flow of ELN entries, experiments, protocol runs, and individual notebook pages, each moving through defined stages of authorship, review, and submission. As the team and its output scaled, keeping visibility over the status of all that work became a job in itself.
Challenge
In a Benchling environment shared across 15 scientists, ELN entries are constantly in motion: in progress, in review, awaiting resubmission, or waiting on a specific owner to act. The only way to know what was stuck and why was for someone to manually check Benchling, page by page. Different entry types carry different turnaround expectations, so when tracking is manual, entries slip past their windows unnoticed, problems don't get corrected on time, and the team loses hours to chasing status instead of doing lab work.
The manual process required someone to repeatedly:
Hunt through Benchling entry by entry to find what was sitting untouched or overdue
Untangle ownership on entries with multiple authors to figure out who was actually responsible
Determine why an entry stalled and had not yet submitted, needs correction, awaiting review
Track differing timelines by entry type, each with its own turnaround and resubmission window
Catch problems before deadlines passed, with no safety net when something fell through the cracks
Solution: Automation Requirements to Production in Under One Month
Karchem Consulting took the automation from requirements to production in under one month, working directly with the client's senior scientist and IT lead to understand both what the team needed operationally and what was technically possible inside their environment. Security was handled collaboratively where the client owns their security posture, and Karchem worked alongside their IT team so the firewall was never a blocker and the automation could run on the client's own server. The result is a fully automated, read-only Python script that reads and reports on entry status without ever editing the notebook itself, keeping Benchling intact as the compliant system of record.
The build broke down into:
Discovery with the right stakeholders; partnering with the senior scientist and IT lead to map real needs and technical constraints
Collaborative security setup; working with client IT so the firewall and server hosting were handled on their terms, with no compliance trade-off
Benchling API integration; connecting to pull the exact metadata that matters: entry type, author, reviewer, current status, and days open
Custom handling of Benchling quirks; Python logic that correctly captures edge cases
Read-only by design; the script reads metadata and sends notifications only; it never edits, moves, or alters an entry
Recipient-specific reporting; filtering the environment and assembling tables routed to the right owners and reviewers, with clear messaging in an aesthetically pleasing layout with direct links to be addressed
Automated weekly delivery; the report runs and emails out once a week on a scheduled trigger
Outcome: 20-30 Hours/Month Back to Science
The team now receives an automated, individualized status report every Monday. Instead of one person manually hunting through Benchling, the script filters the environment, pulls the metadata, assembles tables by recipient, and tells each person exactly which of their entries need attention, what type they are, and how long they've been sitting. On an average week the team logs [X] entries across multiple authors — all now surfaced automatically.
The measurable benefits:
20–30 hours/month of manual tracking eliminated without workflow disruption
Lower cognitive load since no one has to remember to check, cross-reference authors, or chase owners
Reclaimed capacity where scientists spend that recovered time on work that actually moves the needle
