Industries / Healthcare
AI for healthcare operations, with people in the loop.
Voice agents, document automation and assistants that take routine work off your staff, built HIPAA-ready, with a person always one step away.
What we build for healthcare. For the operational work around care, not clinical decisions.
Voice AI agents
Live conversational agents for scheduling, verification and follow-up calls, the work we have delivered for healthcare operations.
Human escalation
Every agent hands over to a staff member when a call needs a person, with the context already captured.
Patient intake and support
Chat and voice assistants that answer routine questions and collect intake details before a visit.
Document automation
Structured extraction from referrals, forms and claims, with only the exceptions routed to people.
Claims workflows
A rule-based workflow for claims, with AI reading the documents and flagging what needs review.
Pharma and life sciences
Triage of incoming drug safety cases and first drafts of regulatory documents, always reviewed by specialists.
Knowledge assistants
Answers from your procedures and policies, with a link to the source for every answer.
HIPAA-ready builds
Access controls, audit logs and patient data handling designed to meet compliance requirements from day one.
Monitoring after launch
Call quality, escalations and accuracy tracked in production, so the system keeps improving.
Match the autonomy to the task. Routine calls can run with supervision. Clinical and approval decisions stay with people.
- L1WorkflowA fixed, auditable path.
- L2AssistantAI drafts. A person reviews.
- L3Supervised agentAI acts and flags exceptions.
- L4AutonomousRoutine, low-risk, monitored.
- Human-ledPeople decideAI supports the analysis.
Appointment scheduling calls
Book, move and confirm appointments, and hand over when a caller needs a person.
L3 Supervised agentPatient verification
Confirm identity and details before a call continues.
L3 Supervised agentFollow-up calls
Reminders and post-visit check-ins, with anything unusual flagged to staff.
L3 Supervised agentClaims processing
A fixed, auditable workflow, with an agent reading documents and flagging exceptions.
L1 WorkflowL3 Supervised agentDrug safety case triage
Incoming cases sorted by a governed workflow, with AI supporting the first assessment.
L1 WorkflowL3 Supervised agentRegulatory document drafting
AI prepares a first draft. Specialists review and own every submission.
L2 AssistantClinical and approval decisions
Diagnosis, treatment and settlement decisions stay with qualified people.
Human-led
Our healthcare work, and the systems behind it. Each result comes from a delivered engagement.
Multi-agent voice AI
Live voice agents for scheduling, verification and follow-up calls, escalating to a person when needed.
~50% fewer manual handling cases AI · Document intelligenceAI document intelligence
Extraction and classification that sends only the exceptions to people.
>80% less manual review AI · Agent platformsAI agent platform
Agents retrieve, reason and act across systems, with human approval before high-stakes actions.
~40% less manual effortHave a healthcare workflow that takes too much staff time?
The technology behind our AI systems. The right stack depends on the model, the data, the architecture and where it runs.
Generative AI and large language models
Models, chosen per task
- OpenAI
- Anthropic Claude
- Google Gemini
- Meta Llama
- Mistral AI
- Cohere
- Hugging Face
- OpenRouter
- LangChain
- LlamaIndex
AI agents and orchestration
Tools, steps and handover
- LangGraph
- AutoGen
- CrewAI
- Semantic Kernel
- Agentic workflows
- Tool calling
- Function calling
- Multi-agent systems
Vector databases and semantic search
Grounded answers from your content
- Pinecone
- Weaviate
- Milvus
- Qdrant
- Chroma
- pgvector
- Elasticsearch vector search
AI integration and APIs
Connecting to your systems
- REST APIs
- GraphQL
- Webhooks
- Microservices
- API gateways
- SDK integrations
- Third-party AI APIs
Cloud and AI infrastructure
Where it runs
- AWS
- Microsoft Azure
- Google Cloud
- Amazon SageMaker
- Azure Machine Learning
- Google Vertex AI
- Databricks
AI evaluation and observability
Quality and cost, measured
- Model evaluation
- Prompt testing
- Hallucination detection
- Latency monitoring
- Cost tracking
- Output quality assessment
- AI observability
- Langfuse
Show the full stack 12 more layersShow fewer layers
Programming languages
What the code is written in
- Python
- Java
- JavaScript
- TypeScript
- C++
- R
- Go
- SQL
- Swift
- Kotlin
AI and machine learning frameworks
Training and running models
- TensorFlow
- PyTorch
- Scikit-learn
- Keras
- XGBoost
- LightGBM
- JAX
- Hugging Face Transformers
Natural language processing
Understanding text
- Text classification
- Sentiment analysis
- Named entity recognition
- Text summarization
- Semantic search
- Question answering
- Embeddings
Computer vision
Understanding images and video
- OpenCV
- YOLO
- Detectron2
- Image classification
- Object detection
- OCR
- Image segmentation
- Facial recognition
- Video analytics
Speech and voice AI
Listening and speaking
- Speech-to-text
- Text-to-speech
- Voice recognition
- Voice assistants
- Conversational voice AI
- Audio processing
Data engineering and processing
Moving and preparing data
- Apache Spark
- Pandas
- NumPy
- Apache Kafka
- Apache Airflow
- Databricks
- dbt
Databases and data storage
Where the data lives
- PostgreSQL
- MySQL
- MongoDB
- Redis
- Microsoft SQL Server
- Amazon DynamoDB
- Elasticsearch
- Snowflake
Containers and orchestration
Deploying and scaling
- Docker
- Kubernetes
- Amazon EKS
- Azure Kubernetes Service
- Google Kubernetes Engine
- Terraform
MLOps and model operations
Keeping models healthy
- MLflow
- Kubeflow
- DVC
- Model versioning
- Model monitoring
- CI/CD pipelines
- Automated retraining
- Performance tracking
Backend development
Services and business logic
- Node.js
- Django
- FastAPI
- Flask
- Spring Boot
- .NET
- Express.js
Frontend and application development
What people use
- React
- Next.js
- Angular
- Vue.js
- React Native
- Flutter
- Android
- iOS
Development and collaboration tools
How we ship
- Git
- GitHub
- GitLab
- Bitbucket
- Jenkins
- Jira
- Postman
Used in our delivered platforms
From first use case to production. Four stages, with a working version reviewed at each one.
-
Discovery
Map the workflow, the systems and data involved, and what good looks like.
Discovery notes
- The calls or documents involved
- Where staff time goes today
- Patient data and compliance requirements
-
Scope & architecture
Agree the architecture, success measures and delivery milestones.
Solution design
- Architecture and data handling
- Escalation rules
- Success measures
-
Build
Build against real data, with a working version reviewed at each milestone.
Working build
- Working agent on real scenarios
- Staff review of every flow
- Accuracy and escalation tests
-
Ship & support
Roll out to production with monitoring and a defined support window.
Production rollout
- Production rollout
- Call and quality monitoring
- Support window
Why Avinya for healthcare. We have put voice AI into healthcare operations.
Delivered in healthcare
A multi-agent voice AI platform for scheduling, verification and follow-up calls, with about 50% fewer manual handling cases.
People stay in control
Human escalation and approval checkpoints are part of the design, not added later.
HIPAA-ready by design
Patient data handling, access and audit trails planned before the first line of code.
Reusable framework
The same framework extends to new call types without rebuilding.
Engagement models. Pick the shape that fits where you are.
Dedicated team
A dedicated team that works as an extension of yours, from first release to scale.
Project-based
A defined scope, milestones and delivery date for a specific product or workflow.
Consulting & advisory
Architecture and readiness advice before you commit to a build.
What our clients say.
“Avinya Labs built our full-stack AI solution, delivering scalable infrastructure, optimized AI workflows, and a seamless user experience. Their expertise accelerated our launch and significantly reduced execution risk.”
“We needed a platform that could handle complex construction workflows without breaking down every week. Avinya Labs delivered exactly that.”
“Avinya Labs built our AI travel chatbot … A highly capable team that delivers ahead of the curve.”
AI for healthcare FAQs.
Is your healthcare AI HIPAA compliant?
We build HIPAA-ready: access controls, audit logs and patient data handling are designed to meet compliance requirements, and agreed with you during discovery.
Does the AI make clinical decisions?
No. We build for operational work such as scheduling, verification, follow-up and documents. Clinical and approval decisions stay with qualified people.
What happens when a caller needs a person?
The agent escalates to a staff member, with the context of the call already captured, so the caller does not start again.
Can it work with our existing systems?
Yes. Agents are designed to connect to the scheduling, records and communication systems you already use. Integrations are agreed during discovery.
Where should we start?
With one task where the data exists and the risk is contained. We map it, agree what good looks like, and build a working version you can review before anything scales.
How do you handle our data?
Access is scoped to what the system needs, with role-based permissions and audit trails where records change. We agree data handling during discovery.
Ready to turn your vision into reality?
Tell us the problem, the data, and where manual effort sits today. We'll map the path from MVP to scale.
Or email contact@avinyalabs.co