We build predictive analytics models, clinical decision support systems, healthcare chatbots, workflow automation, AI-powered transcription, and custom ML models for health data analysis. Every project starts with your data architecture and builds intelligence on top of clinical, wearable, and patient-reported data.

We start with a data readiness assessment: what health data you have, how it's structured, and what AI capabilities it can support. Then we scope the problem, prototype against real clinical scenarios, and build production ML pipelines with monitoring and retraining workflows.

Yes. We integrate AI outputs directly into clinical workflows through FHIR, HL7, and direct EHR connections. Our FHIR MCP Server enables natural language queries against clinical data, and we build provider dashboards that surface AI insights at the point of care.

We've built predictive models for health risk scoring, anomaly detection in wearable data (HRV, sleep, activity patterns), medication adherence prediction, and patient outcome forecasting. Our ZdroVeno project implemented custom algorithms for detecting health anomalies in senior patients with sub-300ms processing.

Yes. We build conversational AI systems for patient intake, symptom triage, care navigation, and clinical follow-up. Our ai healthcare agent implementations handle multi-turn conversations with context from EHR and wearable data sources, not generic chatbot templates.

HIPAA compliance is built into every AI pipeline from the architecture level. Data encryption, access controls, model audit trails, and governance frameworks. Our Notetaker platform demonstrates this approach: full local deployment so no patient data leaves your servers.

We build custom analytics platforms that unify data from EHRs, labs, wearables, and patient-reported outcomes. Population health dashboards, outcome tracking, and custom health metrics (health scores, HRV analysis, sleep quality scoring) designed for both clinical teams and patients.

Yes. We audit your data landscape, infrastructure, and clinical workflows to determine what AI capabilities your current setup can support. You get a clear assessment of data gaps, architecture requirements, and a phased roadmap for healthcare ai implementation.

Data readiness assessment, problem scoping, and model architecture before a single line of training code gets written, the same discipline whether the project is a chatbot or a full predictive model.

Both. Models are only useful once they're inside a product a clinician or patient actually opens, so every engagement includes the interface layer, not just the algorithm.

Most treat AI as a bolt-on to a generic software practice. Every model we ship is trained and validated against real clinical or wearable data, not a synthetic dataset assembled to hit a deadline.

Standalone. Some clients need a single model or agent added to an existing product, without a full platform rebuild around it.

We're headquartered in Wroclaw, Poland, and work with clients across the US and 20+ other countries. English-first communication and time zone overlap are built into how every engagement runs.

Both. A working prototype validated against real clinical scenarios usually comes before any production commitment, so you're not betting a full budget on an unproven model.

Every system we build includes monitoring for model drift and outcome tracking after launch, plus a clear human-in-the-loop point for anything touching a clinical decision.

Yes, that's the norm among the teams we work with. We bring the AI and clinical data expertise so your team can stay focused on the product and the patients using it.

Both, often together. A risk score is only useful if it surfaces inside the workflow a clinician already follows, which is why we build the scoring model and the decision support layer as one engagement.

Yes. Adding a predictive layer, automation, or a conversational agent on top of an existing product without a rebuild is one of the most common engagements we run.