Bioanalytical AI & Lab Operations
We are developing a platform that brings laboratory operations and AI-assisted scientific support into one connected workflow—shaped around the practical needs of bioanalytical teams.
The idea, in plain terms
A laboratory manages much more than analytical results. Teams coordinate studies, track samples, maintain methods, book instruments and search for relevant scientific information—often across different systems.
Our planned platform brings these activities together. One layer organises laboratory work. A second layer—the AI Method Specialist—would help scientists explore method-development and troubleshooting questions using relevant, approved knowledge.
The goal is to support scientific judgement with better context, organisation and traceability.
Two connected layers
A shared foundation for everyday laboratory work and the knowledge that supports it.
A planned workspace for studies, samples, methods and resources. Laboratory information management (LIMS) would organise records and sample histories; resource-planning functions would help coordinate people, instruments and materials.
A planned scientific assistant that considers the analyte, sample matrix and analytical challenge, retrieves relevant reference material, and offers source-linked suggestions for a scientist to assess.
Planned capabilities
These capabilities describe our development direction. The initial focus is small-molecule LC–MS/MS workflows, with broader assay support considered for later stages.
Organise study milestones, responsibilities, protocol versions and progress in one shared view.
Follow sample identifiers, storage locations, aliquot relationships and handling histories.
Find relevant approved methods, SOPs, validation documentation and troubleshooting references.
Explore sample-preparation and chromatography questions in the context of the analyte, matrix and study.
Support structured checks, traceable findings and recorded reviewer decisions.
Coordinate analysts, instruments and materials with appropriate access and version histories.
A closer look at the AI
Retrieval-augmented generation, or RAG, is a way to connect AI responses with a selected knowledge library.
The planned workflow would first retrieve relevant approved documents, then use that context to prepare a response with source references. Scientists would assess the suggestion and decide what to investigate or apply.
Source retrieval supports reviewability. It does not establish that an experimental recommendation is correct or that a method is validated.
Describe the analyte, matrix, method and question.
Find supporting material in an approved knowledge library.
Connect possible next steps to their supporting references.
Review the evidence and make the scientific decision.
Scientific expertise meets engineering
GS Labloure brings its bioanalytical and sample-preparation perspective to the initiative. The development direction combines that domain knowledge with specialist AI engineering.
Source traceability, controlled access, document versioning and appropriate handling of confidential information are design priorities. Their implementation and evaluation will depend on the intended workflow and customer requirements.
We welcome conversations with CROs and laboratory teams to help shape useful, reviewable workflows.
Our development collaboration
Developed in collaboration with our specialist AI technology partner, Mr Shabaz, an AI engineer from IIT Kharagpur.
Both initiatives are in concept and initial development. We are shaping their scope around practical scientific workflows and welcome conversations with interested teams.
A few useful answers
