01
Discovery Sprint
2–3 daysDiscovery Sprint2–3 days
We define the problem space, map constraints, integration boundaries, and success criteria — so the prototype solves the right problem from day one.
Requirements docConstraint mapData audit
Custom healthcare software,
from raw data to deployment
End-to-end custom development and AI integration — we validate your data, train domain models, build production software, and deploy into your PACS, RIS, and EHR environment. Working prototype in a week, not a quarter.
- before slide decks
- Prototype-first
- before slide decks
- DICOM · HL7 · FHIR
- Integration-ready
- DICOM · HL7 · FHIR
- one accountable team
- End-to-end
- one accountable team
Build Console
BuildingData
Train
Validate
Deploy
$ softx pipeline run --env clinic-prod
TypeScript · Python · CI/CDSSO · RBAC · audit logs
The Build Pipeline
From raw data to production deployment
Six stages, one accountable team — from validating your raw data to deploying into your environment.
01
Every project starts with understanding what you have. We profile, validate, and assess your source data so the build starts from a clean foundation — not assumptions.
Schema checks, null analysis, distribution profiling, and anomaly detection across your datasets before a single line of product code is written.
Schema checksDistribution profilingAnomaly detection
02
Models are trained against your real-world data, tuned for your domain, and validated against clinical or operational benchmarks that matter to your team.
Experiment tracking, hyperparameter tuning, cross-validation, and performance benchmarking — all before anything reaches production.
Experiment trackingCross-validationDomain benchmarks
03
Production-grade architecture from day one. APIs, services, and data pipelines built with iterative releases so you see working software early and often.
TypeScript, Python, cloud-native infrastructure, CI/CD pipelines, and automated deployments — built for scale and maintainability.
TypeScript / PythonCloud-nativeCI/CD
04
Interfaces designed around your users' actual workflows. Every screen is purposeful — built to reduce cognitive load and accelerate decision-making.
Design systems, component libraries, accessibility-first patterns, and responsive layouts tailored to clinical and operational contexts.
Design systemsAccessibility-firstClinical UX
05
Comprehensive validation across every layer. Unit, integration, and end-to-end workflows are verified before anything reaches your users.
Automated test suites, regression testing, performance benchmarks, and compliance validation — nothing ships without passing the bar.
Automated suitesRegression testingCompliance validation
06
The final mile: connecting into your existing systems, migrating data, and rolling out with monitoring and support plans already in place.
API integrations, SSO, RBAC, audit logging, health checks, and observability — deployed with confidence into regulated environments.
PACS / RIS / EHRSSO · RBACObservability
Rapid Prototyping
Build, validate, and ship a working prototype fast
We turn initial requirements into a production-style prototype in short cycles — so decisions are grounded in real product behavior, not slides and assumptions.
02
Clickable Prototype
1 weekClickable Prototype1 week
High-fidelity interaction models for stakeholder review — real screens, real workflows, real feedback your team can click through and critique.
Interactive prototypeDesign systemFeedback sessions
03
Technical Spike
3–5 daysTechnical Spike3–5 days
Validate the hard parts before committing: data contract viability, API feasibility, model inference latency, and integration risk factors.
API viability reportRisk logBenchmark data
04
Pilot Build
2–4 weeksPilot Build2–4 weeks
A production-style MVP with the architecture, security patterns, and deployment readiness to move directly into a pilot — not a throwaway demo.
Working MVPDeployment checklistRoadmap
“Decisions grounded in working software — not slides and assumptions.”
Why every engagement starts with a prototype
DICOM / HL7 / FHIRISO-compliant MVPsSaMD support
Case Studies
Custom builds in practice
End-to-end builds that started as raw data and shipped into live clinical environments.
Case Study
Imaging AI Triage in Practice
This case study illustrates how a radiology department integrated imaging AI that SofTx designed and built — CT hemorrhage detection (HemoScan) and ultrasound effusion detection (SynoVision), both developed as client products — into its workflow to improve triage, prioritization of time-sensitive findings, and reduce diagnostic delays. The deployment is representative of how medical imaging AI can be integrated into real PACS and worklist environments.
Read case study
Case Study
Workflow Optimization in Practice
This case study describes how a hospital network and radiology operations group modernized staffing and intake workflows with systems SofTx designed and built — a custom constraint-aware scheduling platform, Pathway Forms, and supporting automation services. Names and specific metrics are anonymized.
Read case studyFAQ
Custom development, without the mystery
Timelines, integration, compliance, and stack — the first-call answers.
Ask us something elseA clickable, high-fidelity prototype lands within the first sprint cycles, right after a short discovery sprint. A production-style pilot build — real architecture, security patterns, deployment readiness — follows on its heels.
Yes — systems integration is a core capability, not a bolt-on. We build against DICOM, HL7/FHIR, and vendor APIs, and deploy into PACS, RIS, and EHR-connected environments with SSO, RBAC, and audit logging in place.
We support regulated builds with verification, validation, and compliance strategy aligned to your jurisdiction — from ISO-compliant MVPs through deployment documentation for clinical environments.
TypeScript and Python on cloud-native infrastructure, with CI/CD pipelines, automated testing, and observability from day one. Iterative releases mean you see working software early and often — not at the end.
That's the most common starting point. Every engagement begins with data validation — profiling, schema checks, anomaly detection — so the build starts from a clean foundation. From there: model training against your real-world data, then product development around your users' workflows.
Either. On-premise edge deployment keeps data inside your network; secure cloud on AWS/Azure scales without infrastructure overhead. Both paths include PIPEDA/GDPR-aligned controls and SOC 2 Type II certified practices.
Have a build in mind?
Bring us the problem and the data you have — we'll map the build, the integration path, and a realistic timeline.
PIPEDA & GDPRSOC 2 Type IIOn-premise or cloud