SwasthyaIQ builds foundational digital systems that bring structure, consistency, and clarity to fragmented healthcare processes — without compromising the human dimension of medicine.
A healthcare technology organisation designing algorithmic frameworks that bring uniformity and accountability to complex, fragmented healthcare activities.
In markets like India — where healthcare delivery spans an enormous range of providers, capabilities, and infrastructure — consistency is both a clinical and operational imperative.
SwasthyaIQ enables that consistency without compromising privacy, medical context, or the empathy that defines good care. We work at the intersection of structured data, algorithmic logic, and healthcare domain expertise.
Our systems are designed to be auditable, interoperable, and sensitive to the deeply personal nature of health information.
We identify repetitive, error-prone healthcare workflows and transform them into auditable digital systems using structured data and algorithmic modelling.
Healthcare data is among the most personal information a person holds. We treat it accordingly — at every layer of every system we build.
Purpose-built tools that address specific inefficiencies in the diagnostic and clinical reporting pipeline.
A standardised, algorithmic approach to diagnostic reporting that improves clarity, consistency, and trust — for both clinicians interpreting results and patients who need to understand them.
By applying deterministic rules to report generation, we eliminate the ambiguity that leads to misinterpretation, follow-up errors, and downstream delays in care.
Complete technical documentation covering request formats, response schemas, authentication flows, error handling, and integration patterns for the Smart Health Report API.
View API Documentation (PDF)Pathology reports are heterogeneous by nature — varying layouts, inconsistent formatting, scanned documents with alignment irregularities, and legacy print artefacts. XtractXpert is a fully proprietary, end-to-end extraction engine that identifies and isolates structured clinical parameters — such as RBC count, platelet levels, liver enzymes, and the full spectrum of haematology and biochemistry markers — regardless of format or scan quality.
Built entirely in-house and independent of any third-party API or external service, XtractXpert is engineered on a continuous improvement model. Accuracy advances iteratively with every release, underpinned by rigorous validation against real-world report variability. Our long-term objective is unambiguous: to be the world's foremost system in pathology parameter extraction.
Developed entirely in-house, XtractXpert operates without reliance on any third-party AI or extraction service, giving us complete control over accuracy, reliability, and confidentiality at every step.
By default, all input documents are permanently deleted after extraction. Integrators may opt in via an API flag to permit retention of submitted reports solely for model improvement. This is entirely voluntary and never the default behaviour.
Retained documents undergo manual PII masking within our internal software framework before any use in training pipelines.
Access to retained data is governed by strict internal controls, restricted to authorised personnel within the improvement programme.
Opting in directly contributes to higher extraction accuracy in subsequent releases — benefiting the broader integrator ecosystem.