Applied AI Research
for Scientific & Technical Domains

Aurora is a custom data pipeline service — we partner with research teams to design, build, and operate automated processing workflows tailored to their specific experimental data. Researchers send us their raw data; we return structured, analysis-ready results in a fraction of the time it would take manually.

The bottleneck in most research isn't collecting data — it's processing it. A single experiment can generate hundreds of images, recordings, or sensor logs that require hours of repetitive manual work before any real analysis begins. Aurora eliminates that bottleneck entirely, enabling teams to scale their research without scaling their workforce.

We work with images, video, audio, and sensor streams. Using combinations of AI models, custom filters, and domain-specific algorithms, we extract meaningful data points — colony counts, band intensities, structural measurements, labels, classifications — and consolidate them into clean databases ready for statistical analysis. Every pipeline run is human-supervised to guarantee consistent, auditable results.

All processed data is stored in our secure cloud infrastructure with access controls, versioning, and export options configured to the research team's needs. Data integrity and confidentiality are treated as requirements, not afterthoughts.

Researchers photograph petri dishes — often with handwritten labels, variable lighting, and multiple dishes per frame. Our pipeline locates each dish, reads and classifies the labels, then proceeds through segmentation to deliver per-dish colony counts, microorganism area, growth rate, live/dead quantification, and any additional metrics defined in the task brief. The entire sequence runs on each new batch of images with stable, reproducible results.

Raw input

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Detection

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Segmentation

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Analysis

Gel scans are processed to detect and isolate individual bands across all sample lanes. The pipeline integrates band intensity, evaluates relative protein separation, and exports lane-by-lane data tables — all using the same core model adapted from the petri dish pipeline, demonstrating how a single architecture generalizes across experimental domains with minimal reconfiguration.

Raw gel scan

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Band segmentation

Schlieren imaging captures plasma-induced refractive index gradients invisible to the naked eye. Our pipeline preprocesses and stabilizes image sequences from optical setups, extracts density gradient and flow structure data, and performs quantitative intensity distribution analysis frame-by-frame. What previously required manual frame inspection across hundreds of images is reduced to a structured numerical output that feeds directly into the research team's analysis workflow.

Raw frame

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Gradient analysis

• Custom pipeline design and development per research task
• Supports images, video, audio, and sensor data
• AI + custom filter chains with cross-domain generalization
• Human-supervised batch processing for quality assurance
• Petri dish colony identification, counting, and growth analysis
• Gel electrophoresis band intensity and lane quantification
• Schlieren plasma flow and gradient extraction
• Automatic structured database and statistical report generation
• Secure cloud storage with versioning and access control