Label and prepare data faster
Turn raw datasets into training-ready assets with assisted labeling, review workflows, structured metadata, and traceable lineage.
Messy data in. Deployable AI out.
Collect, label, train, validate and deploy models in one workflow, with a record of every step from first file to live endpoint.
The missing layer in AI delivery.
ModAstera turns the messy middle of AI delivery into one traceable system: data, labels, model runs, validation evidence, and deployment records all stay connected.
Incoming signals
- DICOM studies
- Inspection images
- Sensor streams
- Reviewer notes
- Existing labels
A model package teams can review, repeat, and ship
Every handoff carries lineage, evidence, and deployment context instead of another folder of disconnected files.
- Model card
- Audit trail
- Endpoint context
Built for teams that need more than a model demo
ModAstera is designed for teams working in operational environments where data quality, workflow speed, traceability, and deployment readiness all matter.
Where most projects stall.
AI in healthcare and manufacturing rarely fails on modeling. It fails on data preparation, validation friction, and tooling that domain experts can't operate. We rebuilt the pipeline around those three failure modes.
High cost & long timelines, by default
Custom pipelines, manual validation, and months-long build cycles drain capital, clinical impact, and manufacturing throughput. Teams spend their time rebuilding pipelines instead of improving the models they already have.
What we see in projects: Months of custom build before the first model ships
Tools too complex for domain experts
Clinicians, researchers, quality engineers, and operations teams shouldn't need to wrangle Kubernetes to label scans, inspect defects, or validate sensor data. ML toolkits dressed up as platforms slow expert sign-off.
Pattern observed: Fragmented stacks, deferred domain sign-off
Data preparation is the bottleneck
Poor labels, inconsistent segmentation, inspection gaps, and missing audit trails make models unsafe to deploy and difficult to certify. The model is downstream of the data.
Where the time goes: Most project time goes into data work
Label, build, deploy. One workflow.
ModAstera brings the stages of AI delivery together, so teams stop losing time to disconnected tools, hand-offs and internal rebuilds.
See MAEA
Built for healthcare. Ready for manufacturing.
Healthcare and manufacturing teams work with different data, reviewers and deployment rules. ModAstera keeps both on one traceable workflow, from data preparation to production.
For Healthcare
Move medical AI from annotation and validation into deployment-ready workflows, with clear lineage, expert review and the evidence reviewers ask for.
Clinical datasets, assisted annotation, model validation and a documented handoff in one path.
For Manufacturing
Turn inspection, sensor and operational data into deployable AI workflows for quality, reliability and production-scale improvement.
Defect inspection, process signals, reliability analytics and repeatable deployment records.
How healthcare teams use ModAstera
Each story shows the bottleneck, what changed inside the workflow, and the operational outcome.
Deploy medical AI without a long platform build
- Bottleneck
- Teams can validate a model but still wait months on infrastructure, security review, and API delivery before clinicians or partners can use it.
- What changed
- ModAstera packages validation, environment controls, and deployment into one workflow so teams move from approved model to a secure endpoint without rebuilding the stack.
- Outcome
- A mobile screening app reached users without a separate infrastructure build.
Prototype research models mid-study
- Bottleneck
- Research groups lose grant time to pipeline setup, experiment tracking, and compute hand-offs before they can test a serious idea.
- What changed
- MAEA lets teams define experiments, compare versions, and manage compute in one place so model exploration happens while the research question is still fresh.
- Outcome
- A Japanese university team cut an MRI brain-age prototype from a 90-day window to less than a week.
Label data without drowning experts in review
- Bottleneck
- High-quality labels are still the biggest delay in medical AI, especially when specialists have to annotate every sample manually.
- What changed
- AI-assisted pre-labeling, review workflows, and traceable edits let specialists focus on corrections and edge cases instead of repetitive first-pass annotation.
- Outcome
- A surgical data company's experts now review AI-drafted labels instead of annotating every slide from scratch.
Screen regulatory files before queues back up
- Bottleneck
- Regulatory screening teams can be overwhelmed by document queues, repetitive checks, and uneven review quality.
- What changed
- Workflow automation screens incoming documents, flags gaps early, and gives staff a more consistent starting point for advisory review.
- Outcome
- Reviewers start from a screened file with gaps already flagged, instead of a raw document queue.
Voices from the field.
What healthcare, research and regulatory teams say about working with ModAstera.
With the support of ModAstera, we have trained a model to learn tongue diagnosis from Traditional Chinese Medicine doctors and classify features from tongue images. By leveraging ModAstera's MAEA, we can build AI models without writing code. For startups like ours, which operate with limited resources, this enables us to reduce development costs and accelerate AI development—making it an especially attractive solution.
ModAstera is tackling one of the most critical challenges in healthcare today—making medical AI truly accessible and scalable. Their approach is not only visionary but also grounded in practical execution. As a medical data company, Surg storage Co.,Ltd. is proud to support such forward-thinking innovators. We look forward to contributing to ModAstera's journey as they continue shaping the future of medical AI.
I have collaborated with the team of ModAstera regarding a development of convolutional neuronal network (CNN) models to predict aging and lifestyle/cardiovascular diseases from medical image phenotypes. The work of ModAstera was fast and has provided CNN models with high predictability. I very much appreciate the contribution of ModAstera with the great supports on our project.
ModAstera's AI technology helped us leverage our domain expertise to build Document Compliance Engine (DCE), a robust AI-powered regulatory compliance product for the pharma industry in Africa and beyond. DCE's capabilities have also positioned us for long-term partnerships with regulators who need more efficient ways to review applications without compromising quality. Without ModAstera's technology, building this product would have taken significantly longer to develop. The quality of insights provided was helpful in refining the product architecture, user experience, and overall design strategy. We see this as a long-term partnership as our business scales and regulatory needs evolve.
What we build.
MAEA is our AI engineering platform for teams that build models. FindCare helps people find trusted clinicians and send clearer consultation requests.
MAEA
The AI engineering platform.
End-to-end AI development and MLOps for high-stakes work: annotation, training, evaluation, governance and deployment in one workflow.
- Medical imaging formats such as DICOM and NIfTI
- Assisted labeling
- Reproducible training
- Subgroup, drift and bias evaluation
- Lineage for every dataset and model
- Edge or cloud deployment
- Federated training option
FindCare
Find trusted clinicians and care that fits.
FindCare helps patients choose a clinician with better context: the concern, location, language and practice, before they request a consultation. It is a clearer front door for independent clinical practices, with clinician judgment kept at the center of care.
- Clinician profiles by service and location
- Each listing shows its source and review status, and fees when declared
- Clearer consultation requests with care context
- Supports clinical review; does not replace clinical judgment
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
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
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