Aiforia

Aiforia provides a no-code AI platform for image analysis in pathology and life-science research, turning microscopy data into quantitative results.

Overview

Aiforia lets pathologists and researchers build custom image-analysis models without programming. Users annotate examples in a browser-based interface, and the platform trains deep-learning models that quantify cells, structures, and patterns across whole-slide images. In diagnostics, its clinical applications support tasks such as cancer biomarker scoring, freeing pathologists from counting cells under a microscope. In research, the same approach accelerates preclinical analysis across tissue types and study designs. Cloud deployment means collaborators can share models and results across sites, and validation tooling supports the accuracy documentation that clinical use requires. By making model creation accessible to domain experts, Aiforia addresses the reality that the hardest part of medical image AI is rarely the algorithm — it is knowing what to measure. The platform scales from single-study analysis to regulated clinical deployment, with audit trails, version control for models, and validation workflows that meet laboratory quality requirements. Support teams assist with model development and validation, which matters for laboratories adopting AI for the first time and needing confidence in measured accuracy.

Key Features

• No-code model training through browser-based annotation
• Whole-slide image analysis quantifying cells and structures
• Clinical applications including biomarker scoring support
• Cloud collaboration sharing models and results across sites
• Validation workflows with audit trails for clinical use
• Model version control supporting laboratory quality and change management

Pricing

Aiforia prices its platform through enterprise agreements based on use case, volume, and deployment model, with research plans accessible for academic groups. Clinical deployments include validation support.

Who Is It For

Pathology laboratories and life-science research teams that generate rich image data but lack machine-learning staff, and organizations standardizing quantitative analysis across multiple sites. It fits domain experts who know exactly what they want measured and need software that respects their expertise instead of demanding Python code.