Challenge

A mid‑size biotech lab working on bioactive peptides was running complex workflows across computational biology and wet lab assays, but struggled to track experiments cleanly from design to data analysis. Experiments were often marked complete when work was still in progress, while similar runs stayed incomplete despite following the same protocol structure. Progress indicators inside the system did not consistently reflect reality, forcing scientists to second‑guess workflow state.

The lab relied on templates and dedicated folders for recurring assays, but the way those templates were embedded and reused led to inconsistent behavior. Protocol instances were sometimes duplicated or embedded in ways that diverged from the intended sequence, which made completion logic hard to trust. Because the system did not reliably show what was truly done, scientists had to manually investigate experiment history, toggle steps, and cross‑check records. This added overhead and made it harder to repeat work confidently, especially when multiple people relied on the same assays.

Solution

Scispot was implemented as the connected lab operating layer, with a focus on fixing real workflow execution rather than just adding another tool. The environment combined structured labsheets, protocol execution, notebook‑based analysis, and automation against a single data backbone. Scispot configured labsheets that matched the lab’s peptide lifecycle and plate‑based assays, turning scattered tracking into one structured view that could be queried and automated.

In joint working sessions, Scispot helped the lab diagnose why completion behavior was inconsistent: protocol instances were being embedded and reused in ways that conflicted with the platform’s intended flow. The team then reshaped protocol usage so templates were embedded in sequence and experiments followed a clearer pattern that the system could represent reliably. While the longer‑term workflow redesign was underway, Scispot proposed immediate workarounds, such as re‑ticking steps to refresh progress, so experiments could continue without blocking day‑to‑day work. In parallel, the teams explored a leaner completion template with straightforward controls, giving scientists a simple, trustworthy way to mark work as done.

The implementation was driven through real usage: live proteomics workflows, structured templates, and concrete assay runs rather than abstract diagrams. Scientists raised issues based on daily friction, and Scispot responded with targeted changes to protocol patterns and automation behavior, making the system feel closer to how the lab already worked. Account‑level signals, such as ongoing usage and connected operations around finance systems, show that Scispot became part of the lab’s regular environment, not just a short‑term pilot.

Result

By turning a vague “the workflow doesn’t work” complaint into a specific root cause and set of next steps, the lab moved from frustration to an improvement plan it could act on. Scientists gained a more predictable way to handle protocol completion, which reduced reliance on guesswork when checking experiment status. The combination of better protocol structure and simplified completion controls gave the team a cleaner mental model for when an experiment was truly complete. That lowered the need for manual reconciliation and made it easier to review experiment history without second‑guessing the system.

With a single operational layer tying experiments, protocols, and automation together, the lab now has a clearer base for expanding into more complex or regulated workflows. Future work, such as adding new assay classes, tightening traceability, or layering analytics on top of lab data, can build on a system that already reflects how scientists actually run experiments, rather than forcing them into a rigid template.