Goldbach Labs
Enhancing Bioinformatic Workflows With Intelligent Automation
Mission Statement and Executive Summary

Product Demo Video

Modern bioinformatics workflows are complex and manual. Goldbach Labs is developing an AI agent to automate these processes. This will streamline workflows and reduce human effort. It enhances accessibility for researchers. Our solution makes bioinformatics pipelines more efficient.
We make bioinformatics as easy as a ChatGPT search, no expertise needed.
"Co-Pilot for Computational Biology"

🧩 What problem are we solving?

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🛠️ What is our solution?

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Why Now?

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Market

🤸 What is the target audience?

Academic & Research Institutions, Biotech & Pharmaceutical Companies, Hospitals & Clinical Genomics Labs, Government & Public Health Agencies, Environmental Research Organizations, and Bioinformatics & Genomics Service Providers. Academic institutions require scalable computational tools for large-scale genomic studies. Biotech and pharmaceutical companies depend on efficient data analysis for drug discovery and regulatory compliance. Hospitals and clinical genomics labs leverage bioinformatics for improved diagnostics and personalized treatments. Government agencies use genomic data for disease surveillance and public health policy development. Environmental research organizations rely on genomic tools for conservation and ecological monitoring. Bioinformatics service providers need cutting-edge frameworks for high-throughput data analysis.

🤔 What were the pain points and common patterns we observed?

Galaxy Cloud Compute or Own Servers Most bioinformaticians we talked to use Galaxy Cloud Compute or have their own servers. Learning Curve Not all bioinformaticians can write code, leading them to spend time and money learning how to use these tools. Irreproducibility and Inefficiency Since this is a manual process with some automation, there is a high risk of inconsistency and inefficiency in data processing. The Need for a Better Solution We spoke to people learning to operate these tools, and many expressed frustration with the complexity and inefficiency of existing methods. Most agreed that an application like this would be beneficial in simplifying the process. Resistance to Change A few still prefer the original methods despite their challenges. Initially, some people resisted new technologies like Uber or ChatGPT, but they eventually became the norm.

🌎 How big is the opportunity?

Total Addressable Market: The global bioinformatics market was valued at approximately USD 20.72 billion in 2023, growing at a compound annual growth rate of 17.6% during this period. Serviceable Available Market : Focusing on bioinformatics software, the market was valued at approximately USD 10 billion in 2023 and is projected to reach around USD 25 billion by 2032, growing at a CAGR of 11% during the forecast period. Market Dynamics: Regional Insights: North America holds the largest market share, accounting for 43.56% in 2022, driven by extensive research activities and healthcare innovations. Europe is noted as the fastest-growing region within this sector but other nations are also catching on. India The Indian bioinformatics market was valued at approximately USD 486.5 million in 2024 and is expected to grow at a compound annual growth rate of 18.62%, reaching USD 2.53 billion by 2033. Loading... Asia-Pacific The Asia-Pacific bioinformatics market was valued at approximately USD 2.92 billion in 2023 and is projected to reach around USD 7.74 billion by 2028, growing at a compound annual growth rate of 21.4% during the forecast period. Loading...

The Competition
⚔️ Who are they and how do we beat them?
Galaxy Cloud Compute
Galaxy is an open-source bioinformatics platform designed for researchers to analyze genomic data without requiring command-line expertise.
Geneious
It is a widely used bioinformatics software suite designed for DNA, RNA, and protein sequence analysis.
DNAnexus
Another bioinformatics platform designed for processing, analyzing, and managing genomic and biomedical data.

Fun Fact:
These companies have yet to integrate a solution like ours, which could be due to several factors. One possibility is that our approach may not be the right fit. However, a key reason could be that they primarily cater to core bioinformaticians and computational biologists, whereas our goal is to make bioinformatics accessible to every biologist.
Additionally, many competitors in this space are open-source, which often correlates with challenges in attracting top developers and innovators. Others have been acquired by PE firms, where innovation tends to slow down. Lastly, the startups in this industry typically focus on serving professionals rather than democratizing bioinformatics for a broader audience.
Challenges to Growth

🏃 What do we want to achieve with this?

We have been exploring this idea for some time, but it was only a few weeks ago after speaking with bioinformaticians that we decided to actively work on it. We are still in the process of verifying whether this solution truly needs to exist, but so far, we have received positive responses from professionals, with a few preferring the manual approach. Our goal is simple: If this is something people genuinely want, as our analysis suggests so far, then our sole focus is to create a gold-standard piece of software for the industry, nothing less. Our mission is to build the perfect bioinformatics tool. However, if we determine after speaking with our customers that this is not a worthwhile endeavor, we hope to fail as quickly as possible and move on to something better. These are our two possible paths each leading to a different outcome, and perhaps, to something even greater. We’ve received a microgrant from Emergent Ventures to kick things off.

💸 Progress So Far?

We have initial market validation, but we want to be more thorough. Additionally, we have outlined the strategy for our initial MVP and have begun early development. However, our approach to the MVP may evolve if we encounter any roadblocks. That said, our final vision remains unchanged—to make bioinformatics simple and accessible for everyone.​ As for our progress, we have not yet worked on the financial projections for a business plan, as we believe it is still too early. However, if we were to make an assumption, we would likely adopt a freemium model, offering a limited number of queries for free, while a premium model would be based on the number of queries and the complexity of the workflows.

Future States
1
Research and Prototyping
Our next steps include finalizing the technical architecture and AI models, developing the MVP with NLP-based query processing, and integrating initial bioinformatics pipelines like FastQC. We will also focus on identifying early adopters and strategic partners while beginning to flesh out a business plan and allocate resources. Given our current capabilities, we believe we have the technical expertise needed for the MVP and won’t require additional hires for now.
2
Beta Development & Internal Testing
We plan to build and deploy the beta version with core AI-driven automation and implement ML-based pipeline selection. Additionally, we will conduct internal testing with research labs and biotech startups. During this phase, we also aim to finalize our business model based on our fleshed-out strategy and potentially establish key collaborations with Research Institutes, Biotech Startups, and Cloud Platforms.
3
Scaling & Pre-Launch Growth
We aim to expand our user base to hospitals, pharma R&D teams, and genomics service providers. Following this, we will focus on improving the product from the ground up, enabling the AI agent to autonomously build pipelines and optimize data processing.
What after that?
A successful launch is the ultimate goal, but to get there, everything needs to go smoothly. For now, let's stay focused and build it step by step.
Who are we?
These are the people who makeup our Nebuchadnezzar.
Farraz Mir
Biotech
Worked on dry lab projects about bioinformatics data processing. Participated in multiple hackathons, exploring the intersection of CS and Bio.
Firas Javid
CS+BIO
Works at a Precision Oncology startup, collaborated with bioinformaticians and made multiple softwares which are in use for cancer care.

Ishaan Chadha
Gen AI+CS
CS Lead at AI Healthcare startup, Collaborated with medical professionals to improve and automate medical workflows across widespread applications.