Industries / Healthcare
AI for healthcare operations, with people in the loop.
AI in healthcare that takes routine work off your staff: an AI voice agent for healthcare calls, document automation and assistants, built HIPAA-ready, with a person always one step away.
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What we build for AI in 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.
Prior authorisation support
Request packs assembled from clinical documents, with gaps flagged for staff.
Referral and discharge letters
First drafts from structured records, always reviewed by a clinician.
Multilingual patient communication
Reminders and answers in the languages your patients use, with escalation to staff.
Match the autonomy to the task. AI in healthcare works best with routine calls supervised. 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 effortWant AI in healthcare to take over a workflow that costs too much staff time?
The technology behind our AI systems. Chosen per task, not per trend.
01 / 08
Models
The language models behind each feature, picked per task on cost, speed and quality.
- OpenAI
- Anthropic Claude
- Google Gemini
- Meta Llama
- Mistral AI
- OpenRouter
- Hugging Face
02 / 08
Agents and orchestration
How agents plan, call tools and hand work to each other.
- LangChain
- LlamaIndex
- CrewAI
03 / 08
Machine learning
Custom models for prediction, vision and classification.
- PyTorch
- TensorFlow
- scikit-learn
- OpenCV
04 / 08
Vector search and databases
Where your data lives, and how answers stay grounded in it.
- Pinecone
- Qdrant
- PostgreSQL
- Redis
- MongoDB
05 / 08
Data pipelines
Moving and preparing data so models see the right, fresh inputs.
- Databricks
- Snowflake
- Apache Spark
- Apache Kafka
06 / 08
Cloud and infrastructure
Where it runs, in your cloud or ours.
- AWS
- Microsoft Azure
- Google Cloud
- Docker
- Kubernetes
07 / 08
Evals and operations
Tracing, testing and monitoring once it is live.
- Langfuse
- MLflow
- GitHub
08 / 08
Product and apps
The product your users actually touch.
- Python
- FastAPI
- React
- Next.js
From first use case to production. Four stages, with a working version reviewed at each one.
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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.
AI platform for an insurance broker
“Avinya Labs built our full-stack AI solution … Their expertise accelerated our launch and significantly reduced execution risk.”
Guides on this topic. Plain answers to the questions buyers ask first.
AI Agent Development Cost: What Drives the Price (2026)
What drives the price of an AI agent: scope, complexity, iterations and retainer.
Read the guide → GuideEnterprise AI Deployment: Why Projects Stall in Pilot
The eight causes we see most, and a roadmap that gets AI live.
Read the guide → GuideOperational AI Systems: A 2026 Guide for Enterprises
How AI moves from pilots into the daily operations of a business.
Read the guide →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.
How long does a healthcare AI project take?
A prototype on a sample call flow or document set takes about 6 to 9 weeks and a first version about 8 to 12 weeks. A production rollout with HIPAA-ready data handling typically takes 3 to 5 months.
Can we sign an NDA first?
Patient information is protected from the first conversation. Yes. We can sign an NDA before you share anything, and we agree ownership of the work in the contract before the work starts.
How do you handle patient data in AI in healthcare projects?
Access is limited to what each task needs, activity is logged, and clinical decisions stay with clinicians. For AI in healthcare we plan privacy duties with the US HHS HIPAA overview in mind and review risk with the NIST AI Risk Management Framework.
Free tool
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Twenty questions, about four minutes. You get a score across five areas and what to fix first.