Service Practice: AI, GenAI & Data
DRUG DISCOVERY | DATA ANALYTICS
MANAGING LEGACY DATA | LAB AUTOMATION
We are on a mission to accelerate the adoption of and impact that AI+GenAI+Data bring to scientific workflows
Why We Exist
20/15 Visioneers stands at the forefront of the powerful intersection of science, data, and algorithms, bringing pragmatic and reliable expertise to help your company unlock the full potential of AI technologies.
By integrating AI, large language models (LLMs), as well as advanced data sets into your scientific workflows, we enhance and optimize your processes. Our tailored solutions don’t just streamline operations—they give your software the extra boost needed to gain a competitive edge in today’s challenging marketplace. Partnering with us means turning cutting-edge technologies into real-world results that drive growth and innovation.
How We Help You
Client Discussions
Technology Guides
Assessment & Design Projects
Software Assets
White Papers
Custom Projects
Benefits of Working with Us
Pragmatic and objective advice from experienced industry veterans
Close integration/interaction with current informatics workflows
Rapid access to leverage large amounts of documents & datasets (internal & external)
Impactful scientific insights for your end-users
Private and low cost, when and how you need it
Partner Success Story: Quine Biologics
Watch how Quine Biologics Founder, Brett Spurrier leveraged LLMs with PythiaAI™ Research Assistant tool.
Read our Technology Guide for more details.
Read on for a deep-dive on LLMs and Agentic LLM Workflows
How LLMs and Agentic LLM Workflows Multiply Up the Value of Traditional AI in Scientific Workflows
Power of LLMs​
Large Language Models (LLMs) excel at interpreting and generating human-like text, automating tasks like data analysis, research synthesis, and personalized communication. Their capacity for learning from vast datasets makes them powerful tools for accelerating scientific discovery, enabling better insights from unstructured data, and enhancing decision-making in lab environments.
Power of Agentic LLM Workflows​
Agentic LLM Workflows combine the reasoning abilities of LLMs with structured processes, allowing them to autonomously execute complex tasks, integrate diverse data sources, and adapt workflows in real-time.
This increases efficiency in labs, accelerates experimentation, and enables seamless automation of repetitive tasks while delivering deeper, actionable insights.
Areas where LLMs can help, and key LLM Functions within
By integrating these functions into scientific workflows, ultimately helping scientists move from raw data to actionable insights more efficiently.
Below are example functions we can deploy for you to integrate into your platform - whether you are developing Data Analytics, Managing Legacy Data, or Lab Automation systems:
LLM Function: Organize, annotate, and explore legacy datasets to unlock new insights and make them usable in current research workflows.
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How It Works: The LLM can parse and restructure legacy data (e.g., older formats, fragmented datasets) into a modern, usable structure. It automatically annotates the data with relevant metadata (e.g., experimental conditions, sample information) and highlights inconsistencies or missing data. The LLM can also identify connections between datasets, making it easier to reuse old data for current experiments.
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Value: Facilitates the integration of legacy data into modern research, reduces time spent on data preparation, and enhances the discovery of insights from historical datasets.
LLM Function: Protocol validation and error detection to ensure adherence to best practices and lab standards.
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How It Works: The LLM reviews experimental protocols for accuracy, completeness, and consistency. It compares the protocol to relevant standard operating procedures (SOPs) or industry standards, ensuring no critical steps are missed. It can also flag potential sources of error or inefficiencies in the experimental design.
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Value: Increases protocol reproducibility, reduces errors, and enhances overall experimental quality.
LLM Function: Real-time protocol monitoring and suggestions for improvement.
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How It Works: As experiments are conducted, the LLM continuously monitors the steps being taken, cross-referencing them with the written protocol. It flags deviations from the original design and suggests course corrections or improvements based on best practices and previous successful experiments.
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Value: Reduces deviations, ensures consistency in lab procedures, and minimizes human error, thus increasing the accuracy and reliability of experimental outcomes.
LLM Function: Tailored report customization for specific audiences (e.g., peer-reviewed journals, internal reports).
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How It Works: Researchers can specify the target audience (e.g., academic journal, regulatory body, internal stakeholders), and the LLM adjusts the report’s complexity and focus. It tailors the language, highlights key insights relevant to the audience, and formats the document according to submission guidelines.
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Value: Speeds up the process of adapting reports for different purposes, enhancing communication and collaboration across departments or with external partners.
LLM Function: Automated hypothesis generation from literature and complex data analysis.
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How It Works: By analyzing literature (such as bioRxiv) and datasets from past experiments or public databases, the LLM identifies correlations, trends, or anomalies. Based on these patterns, it generates hypotheses for new experiments or research directions. It can also provide reasoning behind each hypothesis by linking back to the data or relevant literature.
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Value: Drives innovation by suggesting unexplored areas of research and validating these suggestions based on existing data patterns.
LLM Function: Real-time guidance and support for lab tasks, experimental designs, and data management.
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How It Works: The LLM acts as a digital assistant that provides immediate support for lab activities. It can answer specific lab-related questions (e.g., “What is the next step in this protocol?” or “What buffer should I use for this experiment?”). The assistant can also retrieve and interpret previous experimental data, suggest modifications to protocols, and track experimental progress in real time.
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Value: Enhances lab efficiency by providing researchers with on-demand support, reducing time spent searching for information or troubleshooting.
LLM Function: Automated literature search and summarization.
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How It Works:
The LLM can search scientific databases (e.g., PubMed, arXiv) based on specific research queries and return summaries of relevant papers. It highlights key findings, experimental methods, and conclusions, condensing large volumes of literature into a digestible format.
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Value: Saves researchers significant time when conducting literature reviews and helps them quickly identify the most relevant papers for their work.
LLM Function: Literature gap identification.
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How It Works:
After reviewing a body of literature, the LLM can identify areas that have been under-explored or where conflicting results exist. It generates suggestions for future research based on these gaps, helping scientists focus on unexplored or high-impact areas.
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Value: Increases research efficiency by focusing efforts on understudied areas, potentially leading to novel discoveries.
LLM Function: Streamline collaboration between team members by summarizing ongoing work, sharing key insights, and facilitating data handoffs.
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How It Works:
The LLM integrates with shared digital workspaces and collaboration tools (e.g., Microsoft Teams, Slack, Asana) to provide regular updates on project progress. It can automatically generate summaries of work done by different team members, flagging important milestones or issues, and suggesting tasks that need attention. The LLM can also translate complex scientific data into more accessible formats for non-technical stakeholders.
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Value: Enhances teamwork and communication by ensuring that all collaborators have access to up-to-date information, reducing duplication of effort, and improving transparency across multi-disciplinary teams.
