Machine Learning & Predictive Modeling
Supervised learning, model selection, validation, and interpretation for field, laboratory, and translational settings. Built with strong attention to data quality, honest evaluation, and downstream usability.
Applied intelligence
Small teams. Serious output.
From field trials and climate-linked agronomic signal to omics interpretation and scientific reporting, AidBio emphasizes delivery that survives real operational use.
Pipeline focus
ML + AI
Prediction, automation, and reporting workflows
Domain fit
Agri + Bio
Agritech, biotech, omics, translational analysis
Delivery mode
Scoped
Clear milestones and maintainable code hand-off
Philosophy
Rigorous
No black-box hype without scientific accountability
Core capabilities
Targeted intelligence for technically demanding domains.
AidBio bridges deep biological domain knowledge with modern AI and data-engineering practice, producing systems that are useful to scientists, not just impressive in demos.
Agentic AI Workflows
Domain-aware assistants, structured reporting flows, retrieval pipelines, and lightweight automation that augment small scientific teams without creating infrastructure sprawl.
Agritech & Climate Analytics
Crop-linked and geospatial analytics for field trials, yield-oriented investigation, environmental signal extraction, and weather-aware modeling.
Omics, Bioinformatics & Scientific Data Systems
Support for multi-omics interpretation, biomarker-oriented workflows, experimental data consolidation, and reproducible analysis architecture tailored to research realities.
Projects & open tools
Things we build in the open.
Alongside client work, AidBio ships practical, open-source tools for agritech and life-science data workflows. Open to try right now:
AidWeather
A Python library and CLI that turns NASA POWER weather and solar-radiation data into clean, cached, analysis-ready tables — for a single point, a transect, or a whole region.
Aid2GO
A graph-learning workflow for protein-function prediction that links proteins, protein-protein interactions, the Gene Ontology hierarchy, and experimentally supported GO annotations into one heterogeneous graph, using GraphSAGE and GAT variants over ProtBERT sequence embeddings.
AidViz
A visualization-only toolkit that intentionally does not fetch data or train models: static publication figures, interactive dashboards, and climate storytelling plots built from clean DataFrames, keeping data provenance and presentation responsibilities cleanly separated.
Engagement model
Rigorous process. Zero ambiguity.
Engagements are scoped tightly so teams spend time on science and decisions instead of deciphering vague software deliverables.
01
Define & Align
Clarify the business question, scientific constraints, dataset realities, and decision context before implementation starts.
02
Interrogate Data
Standardize, clean, audit, and explore the data to isolate signal from experimental and operational noise.
03
Build & Validate
Develop reproducible, maintainable pipelines for prediction, interpretation, or structured automation.
04
Ship & Transfer
Deliver documented code, practical outputs, and a hand-off your internal team can actually own.
The founder
Scientific depth meets practical implementation.
AidBio is led by Cleverson Matiolli, PhD — a quantitative biologist and machine-learning practitioner with experience spanning academic research, applied R&D, and computational problem-solving across life sciences and agritech.
The operating philosophy is straightforward: honest modeling, reproducible architecture, and deliverables that remain useful after the presentation ends.
Ready to extract signal from your data?
AidBio engages through well-scoped projects designed to move from messy data to clear operational or scientific decisions.
contact@aidbio.com