Case Study

From Excel Masterpiece to Real-Time Insights in 3 Weeks

About

DMC is a biomanufacturing company developing and producing precision fermentation-based ingredients using its proprietary Dynamic Metabolic Control™ platform. With a focus on sustainable, scalable solutions, DMC enables consistent, cost-effective production of high-value biochemicals for food, nutrition, and specialty markets.

Challenge

Before DMC Biotechnologies started working with BioRaptor, their bioprocess data was buried in a complex Excel file - 20 densely packed Excel tabs × 50+ columns each. Data from on-line, at-line and offline measurements had to be reconciled with calculations spanning across sheets, forming a logic maze that only its creators could navigate.

Scattered data

With data scattered across Excel tabs and formulas dependent on specific sheet logic, only the spreadsheet's creators truly understood how to navigate it. Sharing and interpreting results across the team was time-consuming and risky.

Manual analysis took too much time

Preparing data for review, especially comparing parameters across runs, required hours of prep time. This slowed down the pace of decision-making and experimentation.

Complex calculations

Tracking data across spreadsheets and tabs took a long time and calculations were prone to user-error.

R&D workflows couldn’t scale

As the number of runs increased, process understanding and analysis grew to be longer and more complex. Maintaining the spreadsheet became unsustainable. Adding new data, updating formulas, or reusing logic from previous runs risked errors and inefficiency.

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Solution

DMC was fully onboarded in just 3 weeks, moving from scattered spreadsheets to structured insights almost immediately. In a startup environment where speed matters, that meant faster decision-making from day one. The team didn’t just adopt the platform, they started using it to drive real experiments and gain insights within weeks.

Harmonized historical data

BioRaptor ingested years of online, multi-bioreactor data rebuilding key logic directly into the platform.

Everyone on the team, including scientists, engineers, and managers, can easily access and query the collated data to discover patterns within subsets of the body of experiments. Scientists and engineers can superimpose multiple experimental parameters and sensor readings to find patterns visually.

Complex calculations

The BioRaptor onboarding team reimplemented key calculated fields natively within the platform so there is no need to trace formula logic across tabs.

Self-serve insights

With all experimental data now searchable and structured, team members could visualize trends, explore variables, and extract insights without needing to rely on one or two “spreadsheet experts.” It helped scientists become self-sufficient and get answers faster.

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BioRaptor automates data collection across bioreactors, harmonizes and contextualizes to align all parameters with BioHarvest’s nomenclature.

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BioRaptor automates data collection across bioreactors, harmonizes and contextualizes to align all parameters with BioHarvest’s nomenclature.

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BioRaptor automates data collection across bioreactors, harmonizes and contextualizes to align all parameters with BioHarvest’s nomenclature.

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Results

With BioRaptor, within 3 weeks DMC’s team was able to move faster, collaborate better, and make decisions grounded in real-time experimental data.

3–5 hours saved per run by removing manual prep work, freeing scientists to focus on deeper analysis and design.

With structured and easy-to-visualize data ready for exploration across time points, variables, and batches, scientists can ask better questions and get to insights faster.

Greater confidence in data integrity and collaboration.

Version history, permissions, and audit trails eliminate the risk of accidental data loss and reinforce trust across the team.

Lower data friction lets DMC push sustainable, precision‑fermented ingredients to market faster, cutting both cost and carbon footprint

“BioRaptor has enabled us to ask and answer questions in real time in a way that would have been time-prohibitive with how we navigated fermentation data before. We spend considerably less time processing the data and more time interrogating it.“