Agents, document intelligence and predictive models, scoped to real tasks and reviewed by a person where it matters.
- Healthcare AI
-
Solutions
-
Who We Help
- Case Studies
- Insights
- About
Agents, document intelligence and predictive models, scoped to real tasks and reviewed by a person where it matters.
Healthcare AI works today in administrative and operational tasks, where rules are known and mistakes can be corrected. We scope it there first, then build the product around it.
Interactive execution path for clinical AI inference
Handle routine questions about appointments, preparation, results and billing, using your approved content, with anything unusual passed to a person.
Referrals, intake forms and scanned correspondence turned into structured data, with low-confidence items sent to a review queue rather than guessed.
Multi-step administrative tasks completed within set permissions: eligibility checks, record updates, status chasing. Every action is logged.
Fast answers from your own policies and protocols, with a citation back to the source document so staff can check the answer.
Risk patterns, capacity pressure and care gaps surfaced early enough for someone to act on them.
Relevant information assembled and prioritised so a clinician reaches a decision faster. The decision stays with the clinician.
Every figure here is drawn from delivered work and reviewed before publication. Where a number cannot be evidenced, it is not on this page.
These projects rarely fail on the code. They fail on what was not obvious at the start: a proprietary device format, a consent rule nobody mentioned, or a clinical system that will not be replaced.
Device hubs, streaming telemetry pipelines, patient vitals ingestion, and asynchronous clinician alerting designed for real-world care.
Continuous vitals tracking, threshold violation triage and battery-conscious firmware communications.
Grounded LLM workflows, policy compliance filters, automated document parsing and operational routing.
Bluetooth Low Energy protocols, medical sensor synchronization, edge gateways and secure embedded software.
Secure asynchronous consultations, clinician chat, patient engagement and adherence notifications.
De-identification pipelines, longitudinal patient histories, real-world evidence warehouses and analytical stores.
FHIR R4, HL7 v2 workflows, SMART on FHIR authorization and bridge adapters for legacy healthcare systems.
Full-stack web and mobile products engineered for regulatory scrutiny, rigorous audit trails, reliability and high availability.
A reading that arrives late, attaches to the wrong record or fires an alert nobody reads has no clinical value, however good the device is.
Screens from AI work, with any patient data regenerated as synthetic records.
Most projects need several capabilities at once. Monitoring alone can require device work, a data layer, applications and integration. It is easier when one engineering team holds the whole system.
Custom clinical web and mobile applications designed to operate under strict compliance, with granular permission hierarchies and complete audit trails.
Grounded agents for administrative triage, document intelligence and knowledge assistants with strict source citations and human review.
Resilient device-to-cloud ingestion over BLE, MQTT and cellular gateways, buffering readings through connectivity dropouts.
End-to-end telemetry pipelines from home sensors to triage dashboards with escalation thresholds and synthetic data validation harnesses.
High-throughput transformation pipelines, HIPAA de-identification layers and longitudinal patient data models for operational insights.
Translation layers between legacy HL7 v2 feeds and HL7 FHIR R4 resources, enabling EHR bi-directional synchronisation and SMART on FHIR apps.
We partner with teams that need deep healthcare engineering capability without the multi-month ramp-up of educating generalists on clinical systems.
The practical reasons clients choose us. The evidence sits elsewhere on this site, not in the claim.
We don't sell generic wrapper bots. We build constrained, deterministic architectures with retrieval-augmented generation (RAG) grounded in your validated medical and administrative corpora, complete with confidence scoring and escalation paths.
Most healthcare challenges span Bluetooth firmware, cloud ingestion, clinical UI, and EHR integration. We eliminate the coordination cost of managing four separate vendor teams by unifying full-stack engineering under one roof.
We understand edge buffering, lossy cellular connections, Bluetooth pairing edge cases, and battery optimization across continuous medical sensors, ensuring clinical vitals are never dropped in transit.
We do not build retail apps one week and healthcare platforms the next. Our engineers already understand HIPAA, BAA agreements, FHIR R4 schemas, and the clinical reality of alert fatigue.
You speak directly to the architects who write your code, design your database schemas, and deploy your infrastructure. No junior bait-and-switch or layers of non-technical account managers.
We take clinical technology from early MVP validation through to multi-region cloud deployment supporting millions of daily medical telemetry events without architectural rebuilds.
Profiles reflect the actual engineering leaders responsible for system delivery.
The order rarely changes, though the depth of each stage does. A proof of concept and a regulated device platform follow the same route at very different speeds.
Two questions come before everything else: what happens to the data, and what happens when the AI is wrong.
Every layer of our healthcare infrastructure is designed to preserve confidentiality, enforce attributable access and prevent autonomous AI errors from reaching clinical workflows unchecked.
We design systems to support HIPAA-regulated healthcare environments and work to GDPR, HL7, FHIR and ISO 27001 practices where an engagement requires them. We do not claim certifications we have not been independently audited against.
Encryption in transit and at rest, role-based access, data minimisation and hosting location decisions are built into the architecture from the beginning.
Every action against patient data is attributable to a user or service with immutable, time-stamped telemetry.
Review points are determined by what the output affects. High-risk decisions require named clinician authorization.
AI responses cite approved source material, allowing clinical staff to audit the underlying evidence immediately.
Security testing, process testing and continuous AI evaluation before release and throughout live operation.
Controls are clearly divided between Pashupatastra, cloud providers and clients across the shared security model.
Direct feedback from clinical engineering leaders, health system CTOs, and MedTech founders who run our software in clinical production.
“Pashupatastra engineered our continuous cardiac telemetry infrastructure from raw Bluetooth packets to FHIR R4 observations. Their understanding of low-power mobile Bluetooth reconnection and HIPAA BAA requirements gave our clinical advisory board complete peace of mind.”
“Converting legacy HL7 v2 ADT/ORU messages into SMART-on-FHIR apps that launch inside Epic and Cerner was our biggest bottleneck. Pashupatastra resolved our schema impedance without disturbing hospital clinical staff.”
“Unlike standard AI agencies peddling loose chatbot prompts, Pashupatastra engineered deterministic validation chains and clinician-in-the-loop escalation rules. Our doctors trust the outputs because every inference cites clinical guidelines.”
Hosting rules, national health registries, local cloud sovereignty, and procurement differ profoundly across jurisdictions. We audit regulatory architectures country-by-country before engineering code.
Speak directly with senior healthcare technology specialists about architecture, interoperability, AI, compliance, integrations, and your next product milestone.
Share what you're building and discuss the technical path forward with our engineering team.
Talk to Our Engineering Team