Galatek Veda: Closing the Loop Between the Dry Lab and the Wet Lab_Life Sciences_Galatek

The Gap in Traditional Lab Software

Most lab software treats each experiment as a fresh start. A method runs, generates data, and that data gets filed away. The next method begins from scratch, informed only by the scientist's experience. That is the gap Galatek Veda is designed to close.

Three Components, One Workflow

Veda connects the dry lab and the wet lab into one single workflow, with three modules handling distinct parts of the process. This is physical AI in practice: planning grounded in data, carried through to instruments that act on it.

• The lab informatics module manages inventory, samples, registries, and electronic lab notebooks, keeping research context in one place from the start

• The orchestration module coordinates equipment, schedules tasks and executes confirmed protocols

• The data management module organizes and analyzes experimental data

Execution: Orchestration and the Physical Layer

A method begins with a goal. Working from the context the lab informatics module keeps in one place, a researcher defines the workflow and the instruments it involves. Once that workflow is approved, the orchestration module coordinates the connected lab equipment, executing it across preparation, transfer, and analysis. This is where the workflow transitions from the dry lab to the wet lab.

The Feedback Loop: How Evidence Carries Forward

As the method runs, the data management module ingests the data that each device generated, along with its associated metadata and context, and organizes it into evidence for future experiments. In doing so, it brings the results of the wet lab execution back into the dry lab planning process. The researcher then uses that evidence to inform the next method - what worked, what did not, and under what conditions are all carried forward, ensuring that each subsequent run starts with more information than the last.

Over time, this transforms a lab's experimental history into a living resource, rather than a collection of disconnected datasets. Every result contributes to the knowledge base that carries forward into the next method.

Human oversight by design

A human remains in the loop throughout. The researcher defines and approves each workflow; the orchestration module executes only what’s been confirmed. That review step stays central to how Veda works, regardless of how much of the planning process becomes automated over time.

What’s Next: Gera

The next step in Veda’s roadmap is Gera, an AI research agent that will take on the planning work a researcher does manually today. Given a goal, Gera will interpret it, retrieve the relevant prior evidence, and generate a workflow spanning the instruments and devices involved, automatically linking each step into a cohesive process – all subject to the same researcher review that governs each workflow in Veda now. Gera doesn’t change the loop that Veda already closes; it changes how much of the planning inside that loop happens automatically.

This closed-loop principle, where data informs the next step instead of sitting idle after collection, reflects Galatek's broader approach to AI-driven automation. Veda demonstrates how this approach can be applied to a single workflow, from goal to execution and onward to the next objective.

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FAQS

What is Galatek Veda?

Galatek Veda is an AI-driven software platform for life science laboratories. It brings together lab informatics, data management, and orchestration capabilities to connect experimental planning, researcher review and approval, execution, and results within a unified workflow. Veda’s roadmap also includes Gera, an AI research agent that will take on planning tasks that researchers currently do manually.

How does Veda complement a scientist’s decision-making?

Veda keeps experimental context, data, and results connected, so researchers can review relevant evidence and develop experimental steps and parameters with more complete information. Researchers evaluate and approve each workflow before execution, ensuring that scientific judgment remains with the researcher. Once Gera is live, it will help surface that evidence and propose experimental steps directly, with the same researcher review and approval still governing execution.

How does Veda use past experimental data to plan new methods?

Veda preserves experimental results together with their associated data and research context, making previous work easier to review and reuse. Researchers draw on that evidence today when planning subsequent experiments, evaluating what aligns with their research objectives. As a result, each new method can build insights gained from previous work. Once Gera is live, it will draw on that same evidence to propose experimental steps directly, with researchers still evaluating and approving each recommendation.

What is "physical AI" in the context of lab automation?

In the context of lab automation, “physical AI” refers to the connection between data-driven planning and physical actions performed by laboratory equipment. Within Veda, a researcher plans a workflow informed by prior data and context, reviews and approves it, and the orchestration module coordinates execution across connected instruments and devices. This creates a continuous link between planning and real-world laboratory execution. As Gera comes online, it will take on more of the planning step itself, proposing workflows for that same researcher review.