Label and prepare data faster
Turn raw datasets into training-ready assets with assisted labeling, review workflows, structured metadata, and traceable lineage.
Where most projects stall.
Regulated AI rarely fails on modelling. It fails on data preparation, validation friction, and tooling that healthcare and manufacturing 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. Most teams ship one workflow in the time they should be improving five.
Industry baseline: Over $100K per project · 6 month median delay
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: ~64% of project time spent on data work
Label, build, deploy. One workflow.
ModAstera brings the critical stages of AI delivery into a single operating surface so teams stop losing time to disconnected tools, handoffs, and internal rebuilds.
See the platform
Two regulated paths. One operating surface.
Healthcare and manufacturing teams face different data, reviewers, and deployment constraints. ModAstera keeps both paths on one traceable workflow from data preparation to production readiness.
For Healthcare
Move medical AI from annotation and validation into deployment-ready workflows with clearer lineage, expert review, and regulatory evidence.
Clinical datasets, assisted annotation, model validation, and audit-ready handoff in one path.
Explore healthcare solutions
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.
Explore manufacturing solutionsHow medical 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 AI-assisted diagnosis app launched in 30 days instead of stretching into a seven-month deployment cycle.
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 surgery data company reduced annotation time per slide from hours to minutes.
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
- A backlog that had stretched for months was cleared in days, with stronger submissions and fewer avoidable rejections.
Voices from the field.
Healthcare, manufacturing, research, and regulatory teams use ModAstera to move faster than they thought possible.
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.
Regulated AI that proves itself.
MAEA is the platform we sell. Hebra is one specialist product built on it, proof that the same workflow can ship real domain services while supporting both medical and manufacturing AI programs.
MAEA
Regulated AI Engineering Agent.
End-to-end AI development and MLOps for high-stakes workflows. Annotation, training, evaluation, governance, and deployment in one operating surface.
- Medical imaging and industrial data native
- Assisted labelling agents
- Reproducible training
- Subgroup, drift, and bias evaluation
- Audit-grade lineage
- Edge or cloud deployment
- Healthcare and manufacturing quality evidence
- Federated training option
Hebra
A digital front door for specialist clinics.
Hebra helps clinician-led independent practices turn patient interest into structured, consultation-ready cases through a branded entry experience, stronger intake, and specialist-care workflow.
- Built for independent specialist practices
- Structured intake and image-aware case readiness
- Preserves the clinician's own brand and patient relationship
- Not an enterprise EHR, generic telehealth stack, or marketplace
There's a plan for every team size
Select the perfect plan for your healthcare AI development needs
Starter
Try out the full features for 14 days. No payment needed.
- Manage datasets and annotate with unlimited AI-assist
- Train unlimited predictive models
- Storage up to 10 GB
- Deploy models in one-click with API access
- Collaborative workspace with teammates
- GPU-based model training
- Organization-level administration
Team
PopularPerfect for researchers and small teams
- Manage datasets and annotate with unlimited AI-assist
- Train unlimited predictive models
- Storage up to 10 GB
- Deploy models in one-click with API access
- Collaborative workspace with teammates
- GPU-based model training
- Organization-level administration
Enterprise
For large-scale deployments
- Custom setup
- Unlimited everything
- Dedicated support
- Custom integrations
- SLA guarantees
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See where your AI workflow is slowing down
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