Biostratica AI provides a machine‑learning platform that ingests fossil, lithology and geochemical data to automatically generate biostratigraphic correlation models and age estimates for sedimentary layers. The system delivers confidence‑weighted results with interactive visualizations and export options compatible with GIS and reservoir modeling tools, helping geologists and paleontologists speed up and standardize stratigraphic dating.
Funding
Funding not disclosed
Founders
Product
Problem
Geologists and paleontologists must manually correlate fossil assemblages and lithostratigraphic markers to assign ages to sedimentary layers, a process that is time‑consuming, subject to interpreter bias, and often limited by the volume of data available.
Solution
Biostratica AI offers a machine‑learning platform that ingests geological and paleontological datasets—such as fossil occurrence tables, stratigraphic logs, and geochemical profiles—and automatically generates biostratigraphic correlation models. The system applies supervised learning algorithms trained on curated reference sections to predict age ranges for new sequences, producing confidence‑weighted age estimates. Users can upload raw data through a web interface, where the platform normalizes formats, validates taxonomic consistency, and aligns samples to the most appropriate global biostratigraphic zones. Results are delivered as interactive correlation charts and downloadable reports that integrate with existing GIS and reservoir modeling tools, allowing exploration and research teams to accelerate decision‑making while reducing manual interpretation effort.
Target Audience
Primary customers are exploration geologists, academic paleontologists, and research teams that require rapid, reproducible biostratigraphic dating of sedimentary sequences for resource assessment or scientific studies.
Features
- Automated ingestion and standardization of diverse fossil, lithology, and geochemical datasets
- Supervised AI models trained on globally recognized biostratigraphic reference sections for age prediction
- Confidence scoring and uncertainty quantification for each correlation result
- Interactive visualizations of stratigraphic columns with AI‑suggested zone boundaries
- Export of correlation outputs in formats compatible with GIS, petrophysical, and reservoir simulation software
- API access for integration into custom exploration workflows and data pipelines