How did Quartic.ai’s focus shift in 2024?

Our key product developments in 2024 focused on DataOps. We found that most customers—including big pharma—were not ready with their data, despite significant investments in cloud and analytics. Many are at an inflection point where they need applications but lack the necessary data foundation. We always had industrial DataOps and data contextualization, including CFR21 compliance, as part of our platform. However, we previously positioned it as a supporting capability rather than a standalone offering. We realized customers’ internal data initiatives aligned with what our DataOps platform already provided. This led to an "aha" moment: we needed to lead with DataOps as a core capability.  

We opened up our data platform—not just for our applications but also for customer-developed solutions. Many customers work with AWS and Snowflake, so we strengthened our collaborations with them. 

Beyond DataOps, our focus has been on core multivariate analytics (MVDA, MSPC). Regardless of whether a practitioner is in manufacturing, product development, or quality monitoring, these capabilities are essential for deviation monitoring, CPV, and first-time-right initiatives. While modern ML and AI enhance these processes, no true end-to-end solutions exist in the market. We had MVDA and optimization solutions as standalone offerings, but now we evolved them into a comprehensive workbench for practitioners. Moving forward, our strategy is centered on three pillars: DataOps, advanced multivariate analytics, and opening our platform to integrate seamlessly with customers' existing ecosystems.  

Which market shifts are driving product development?

Over the past years, companies made significant investments in digital transformation. At the macro level, impatience is growing. Management expected a return on investment (ROI) by now, whether from AI or digital transformation initiatives. This led to a pullback in funding for data platforms and digital capabilities. Internally, teams tried to build solutions in-house, but progress was slow. 

Management recognized that these efforts were not moving fast enough. There was a push-pull dynamic between in-house IT teams and business leaders demanding faster results. This urgency drove our strategy. Customers can now use our out-of-the-box DataOps capabilities while integrating with their existing cloud infrastructure—AWS, Snowflake, or Databricks. They can layer our applications on top of their investments to accelerate ROI. The market shift toward faster value realization shaped our approach. Our solutions help companies move quickly without abandoning past investments.

Where is Quartic.ai having measurable impacts on ROI? 

The biggest impact is on product batch release and shipment time. Real-time and predictive product measurements directly affect what is shipped, making ROI easy to track. Many customers face bottlenecks in product development due to high costs and limited personnel. Digital transformation helps accelerate drug pipelines by reducing workload and increasing efficiency. ROI is clearest at two points in the product lifecycle: development and shipment. On the front end, it is measured by how quickly a molecule moves into production, whether internally or through a CDMO. Fewer wet lab experiments and qualification runs mean faster progress. On the back end, shorter batch disposition and investigation times show clear value. Yield improvement, long-term quality, and product robustness remain important but take longer to measure. 

What is the level of AI maturity across the life sciences industry?

Companies hesitate to adopt AI for high-volume, legacy molecules, even when improvements are possible. Risk avoidance takes priority. AI adoption is highest in two cases: early in the product lifecycle and when it impacts quality, throughput, or reliability. New molecules and processes create more openness to AI. Companies want to bring new products to market faster. Since these processes and equipment are untested, there is more flexibility to experiment before scaling to full production. The product development and qualification phase lends itself to experimentation, leading to higher AI adoption.

How close is the industry to achieving autonomy in manufacturing?

Full autonomy in production is still far away. Every major pharma and biologics company has an internal project for an autonomous bioreactor, but most remain in the aspirational phase. Progress has been slower than expected, but the goal has not changed. Fermentation and bioreactors are the starting points for most companies. Widespread adoption remains uncertain, but autonomy remains a long-term priority on their roadmaps.