MAEA

Build and deploy medical AI in one workflow.

Prepare data, annotate faster, train models, validate performance, and deploy with a record of every step.

MAEA is ModAstera's end-to-end platform for medical AI teams. It brings together annotation, experimentation, evaluation, deployment, and documentation so teams stop rebuilding the pipeline around every project.

MAEA Platform

AI engineering platform

Research

Model Training and evaluation

Model Deployment

Why teams switch from fragmented workflows

Medical AI teams often juggle separate annotation tools, experiment infrastructure, deployment handoffs, and compliance documents. MAEA brings those stages into one workspace so projects move with fewer delays and fewer specialist bottlenecks.

Fragmented workflow	MAEA workflow
Annotation, training, and deployment live in separate tools.	One platform connects dataset preparation, model work, and deployment.
Teams lose time to manual experiment setup and repeated handoffs.	Experiments, validation, and iteration happen in one continuous flow.
Operational and compliance records are assembled after the fact.	Traceability and documentation stay close to the work as it happens.
Domain experts depend heavily on scarce ML infrastructure support.	Researchers, clinicians, and technical teams can collaborate in the same workspace.

Annotation, training, and deployment live in separate tools.

Teams lose time to manual experiment setup and repeated handoffs.

Operational and compliance records are assembled after the fact.

Domain experts depend heavily on scarce ML infrastructure support.

Annotation

Start with training-ready data

High-quality medical AI starts with high-quality data preparation. MAEA includes AI-assisted annotation workflows so teams can create cleaner datasets without turning labeling into the whole project.

Faster annotation with assistive labeling and review loops
Cleaner datasets with lineage, version history, and auditability
A smoother handoff from dataset preparation into training and deployment

Annotation capabilities inside MAEA

Few-shot assisted labeling

Start with a small set of examples and expand to larger datasets with AI-generated draft labels.

Active learning and review

Focus expert attention on uncertain cases, approve corrections quickly, and improve the next batch.

Quality control for clinical data

Support review flows, dataset versioning, and traceability that help teams prepare for downstream evaluation and compliance work.

How the annotation stage works

Upload and frame the task

Bring in imaging or clinical datasets and define the labeling objective for your project.

Review AI-assisted drafts

Experts correct boundaries, approve suggestions, and focus on the cases that need human judgment.

Move directly into training

Use the prepared dataset in the same product flow to train, evaluate, and deploy models.

Supported data and modalities

Radiology: X-ray, CT, MRI, ultrasound, and DICOM/NIfTI-derived image exports

Pathology: whole-slide imaging workflows and tiled image review

Ophthalmology and other visual modalities such as fundus or OCT imagery

Annotation types such as segmentation, detection, classification, and keypoints

Start small. Scale when it works.

Every project starts with a conversation. We agree scope and pricing with you before any work begins.

Initial discussion

A call to understand your data, your goal and your constraints.

Your data types, volume and where they live
What the model needs to do, and for whom
Review, privacy and deployment requirements
A clear next step, whether or not we work together

Scoped pilot

One use case, taken from your data to a validated model on MAEA.

Scope, timeline and success measure agreed up front
Labeling, training and validation on MAEA
A model card and validation record
A recommendation on whether to go further

Ask about a pilot

Larger program

Several models, teams or sites, with deployment and support agreed per contract.

Multiple use cases or sites
Deployment to your cloud or edge environment
Service levels agreed per contract
A dedicated support contact