Unlock the Lab of the Future with Scispot's Lab-as-Code Solution

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Unlock the Lab of the Future with Scispot's Lab-as-Code Solution

The future of biotech is not just in the data but how that data is generated, managed, and utilized. Welcome to the era of the "Lab of the Future," enabled by "Lab-as-Code" technology. At Scispot, we've catapulted over 100 biotech firms into this new age. But what does "Lab-as-Code" mean, and how can Scispot help you make this futuristic vision a reality today? Let's dive in.

What is Lab-as-Code?

Think of "Lab-as-Code" as the next-level evolution of the modern lab. Just as Infrastructure-as-Code revolutionized IT operations, Lab-as-Code transforms lab management. It enables the design, execution, and control of lab experiments through programmable code. Data from instruments and experiments are not only collected but also cleaned, transformed, and prepared for advanced analytics, ML, and AI applications, all in a seamless, automated workflow.

How Scispot Enables Lab-as-Code

Self-Serve Integrations

At Scispot, we understand that no two labs are the same. Our platform lets you easily integrate with your existing setup, be it Benchling, Snowflake, LabVantage, Veeva, lab instruments, AWS S3, or MongoDB. One of our clients was able to cut their data preparation time by 50% by integrating their Snowflake and Benchling, LabVantage, and Veeva accounts. It's about giving you the power to build your lab, your way.

Data Readiness

Data is the new oil, but like crude oil, it needs to be refined. Our platform comes embedded with Jupyter Notebook and R Studio, integrated with Github, to automatically clean, transform, and aggregate data. One client used our features to boost the accuracy of their predictive model by 20%. Imagine the edge this could give you in speeding up drug discovery or decoding complex biological systems.


Forget the tedious, manual data entries and labor-intensive setup of experiments. Our API-driven Electronic Lab Notebooks (ELN) and Laboratory Information Management Systems (LIMS) allow you to design, execute, and control experiments programmatically. A leading genomics lab that partnered with us was able to automate 80% of their workflows. This freed their talented researchers to do what they do best—focus on science.

Launch YC: 🚀 Scispot's Lab-as-Code solution: Turn your Biotech into AI Powerhouse

The Labs of the Future: Traditional vs. Lab-as-Code Paradigms

The dawn of a new era in biotech is upon us, and it's called Lab-as-Code. This revolutionary approach is reshaping how scientists conduct experiments, interpret results, and share knowledge. But how does Lab-as-Code compare to traditional lab setups? Here we dive deep into the key differences, bringing scientific rigor to our analysis. 

Here's the table with real-world examples for better understanding:


Traditional Lab

Lab-as-Code (Scispot)

Data Collection

Handwritten notes, often transcribed to Excel later. Ex: Lab notebooks filled out by hand.

Automated data collection from Benchling and other tools. Ex: Data auto-synced from a mass spectrometer.

Experiment Design

Use of paper protocols or simple software like Microsoft Word. Ex: Handwritten experimental setups.

Programmatically designed using API. Ex: JSON files that can be interpreted by machines.

Sample Preparation

Manual pipetting, requires multiple researchers. Ex: Manual aliquoting for cell cultures.

Automated with Jupyter Hub, single operator oversight. Ex: Auto-generated aliquots.

Data Management

Data stored in isolated spreadsheets. Ex: Different Excel files for each experiment.

Unified data management on a single platform. Ex: All data accessible in a dashboard.


Manual calculations using Excel or specialized software. Ex: Manually calculating IC50/EC50 values.

Automated scripts handle complex calculations. Ex: Auto-calculating standard curve equations.


Manual setup and operation. Ex: Manually setting up a PCR machine.

API-integrated devices. Ex: Spectrophotometer setup via API calls.

Workflow Automation

Use of various software for disjointed steps. Ex: Different software for each stage like SPSS, Prism.

End-to-end automation within the platform. Ex: From sample preparation to data analysis in one workflow.


Errors manually identified and corrected. Ex: Noticing miscalculations during peer review.

Automated quality checks flag anomalies. Ex: Auto-detection of outlier data points.

Data Readiness for AI

Data needs to be manually cleaned and transformed. Ex: Extra steps to prepare data for machine learning.

Data auto-transformed, ready for AI. Ex: Auto-cleaning and formatting data for immediate ML analysis.


Difficulty due to manual steps and lack of version control. Ex: Hard to replicate exactly the same experimental setup.

Code-based specs enhance reproducibility. Ex: Version-controlled experiment design.


Slower due to manual steps and inefficiencies. Ex: Spending hours in data preparation and calculations.

Faster automated execution. Ex: Cutting data prep time by 50%.


Increasing sample size means linear increase in manual labor. Ex: More samples, more manual pipetting.

Easy to scale using automated processes. Ex: Double the sample size with no increase in manual labor.

Scispot's lab-as-code solution is the best stack for your company

Taking Lab-as-Code a Step Further: The CI/CD Pipeline 

Just when you thought Lab-as-Code was the final frontier, there's another game-changer: the CI/CD Pipeline. The concept of Continuous Integration/Continuous Deployment isn't new to the tech world, but its application to modern labs is revolutionary. Consider a genomics lab that moved beyond Lab-as-Code to integrate a CI/CD pipeline. When they sequence a new genome, it automatically triggers a chain of processes—from data cleaning to complex analysis—all automated and quality-checked at each step. This cut their data processing time in half and reduced human errors by 30%. In this way, Lab-as-Code becomes a stepping stone towards evolving your lab into a full-fledged CI/CD pipeline, where every element—from data collection to analysis—is automated, efficient, and error-free.

Scispot's Lab as Code flow


The Lab of the Future isn't just an abstract concept; it's a reality we're forging today. Imagine a world where your biotech lab becomes a nexus of innovation, a place where data doesn't just inform decisions but actively drives them. With Lab-as-Code, we are on the cusp of this transformation, and the CI/CD Pipeline catapults us into a realm where seamless automation and near-zero human error are the norms, not the exceptions. This is not just efficiency; it's a revolution, propelling scientific endeavors to scales and speeds previously unimaginable. Now, what if we told you this future is accessible right now? With Scispot's sophisticated data infrastructure, you're not just adapting to the future; you're creating it. Ready to become a pioneer in this new age of scientific research?  Book a free consultation call with our computational biology expert to embark on your journey to the Lab of the Future.

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