Episteme99

Software-guided acceleration
of precision imaging inference

Machine-driven intelligence relies on abstractions of reality derived from spatial, temporal, and visual perception. We envision a future where real-time sensor data directly drives dynamic decision-making, automation, and control. We are building toward that future. Episteme99 is a small German startup based in Hamburg and supported by an incubation grant.

We are developing next-generation classical and quantum software for precision imaging analytics, and inference. To enable scalable utilization of sensors and imaging hardware, feature-rich datasets must be processed with optimal latency. This requirement creates a Pareto frontier between computational accuracy and processing time. Achieving the optimal trade-off is a critical prerequisite for real-time operando machine learning, model interpretability, and high-fidelity reconstruction. Our algorithms and specialized software tools help users efficiently navigate this Pareto frontier.

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Sensor data analytics for
inference pipelines

Autonomous operation of sensors and imaging hardware is a major constraint in delivering efficient point-of-care services. Our development focuses on application-specific, low-latency, improvable-accuracy algorithms to deliver fast yet reliable imaging analytics. Accelerating decision-making makes each operational cycle more efficient. We are exploring three verticals while navigating toward product-market fit.

Exploration

Advanced light sources, such as free-electron lasers (FELs) and next-generation synchrotrons, routinely map unknown macromolecular structures at unprecedented spatial resolutions. On-demand delivery of detector-data analytics can significantly accelerate identification and screening within the drug discovery pipeline. To support this, we are developing sorting algorithms designed to reduce the number of data frames required for downstream applications e.g., reconstruction.

Verification

Unaccounted errors regularly introduce production challenges within semiconductor fabrication pipelines. Automated visual inspection of these defects can be accelerated by providing high-throughput, accurate analytics for batch datasets. To address this, we are developing low-latency discriminator algorithms that exploit spatio-temporal features of the datasets to enable anomaly detection. The resulting inference can enable rapid deployment of corrective measures.

Diagnosis

The automated delivery of biomedical diagnostics is often hindered by inadequate featurization of microscopy datasets. Accurate real-time algorithms focusing on precise feature identification can facilitate both interpretable classification and rigorous comparison with established benchmarks. To achieve this, we are developing specialized tools to enhance the classification of existing datasets. In the future, we plan to extend these tools to include filtering and feedback control.

Sensor data analytics for foundation models in Life-Sciences

We build customized algorithms and architectures that enable faster analytics and precise inference, ideally suited for high-throughput sensing and imaging. We primarily leverage high-dimensional pattern recognition, physics-informed data compression, and unsupervised learning techniques. Currently, our efforts are focused predominantly on microscopy workflows that generate the datasets necessary for building foundation models in the life sciences.