We have shipped telemedicine platforms (Twilio Video and Daily.co WebRTC for video sessions, secure HIPAA-compliant messaging, HL7 FHIR R4 EHR integration), remote patient monitoring systems (CGM, BPM, pulse oximeter device integrations, automated alert workflows, 25+ clinics enrolled in 60 days), and wellness apps used by 2,000+ patients across the US, UK, and Ireland. That domain knowledge shapes every architecture decision from week one: we know which FHIR resource types to use for a given clinical workflow, how to structure PHI data to minimise audit surface, how to handle session continuity in telehealth on unstable mobile connections, and what compliance controls an Epic or Cerner integration requires before the EHR vendor's app review team will approve your application. You do not pay for us to learn what HIPAA Technical Safeguards mean -- we arrive with that knowledge and apply it immediately.

Milestone-based pricing with the fixed total agreed and documented before development starts -- not a time-and-materials contract where the final number depends on how many hours the team logs. The project is broken into milestones (typically 4-6 for a 12-week engagement) with a defined deliverable and payment at each: discovery complete, core architecture reviewed, first working demo, UAT-ready build, production deployment, post-launch stabilisation. You see working software at every bi-weekly sprint review, not at the end of a 14-week silence, so course corrections happen early when they cost a sprint adjustment rather than a project restart. If scope changes, the change is costed and approved explicitly before any additional work begins -- no invoice at the end for "additional requirements."

No handoffs between a business analyst who scoped it, a design team who doesn't know the clinical workflow, and an offshore build team who has never spoken to the client. The same engineers who participate in the discovery sessions, ask the questions about how your nurses actually use the EHR, and review the wireframes with your clinical team are the engineers who write the code. When an implementation question arises at week 8 about how to handle a specific FHIR resource mapping edge case, the person who answers it is the same person who discussed the clinical requirement in week 1. Accountability does not get diluted across a handoff chain.

React and Next.js for web interfaces and patient portals; React Native for cross-platform mobile apps (iOS and Android from a single codebase); Python (FastAPI or Django) for backend services and clinical data processing; PostgreSQL with row-level security for PHI storage; AWS for infrastructure (ECS Fargate for containerised services, RDS for the database, S3 with server-side encryption for document storage, CloudWatch for monitoring and alerting). Health-specific integration stack: HL7 FHIR R4 for EHR connectivity (Epic App Orchard, Cerner FHIR R4, Athenahealth API); Twilio Video and Daily.co for HIPAA-eligible WebRTC video sessions; Twilio SMS for appointment reminders and patient notifications; Stripe for patient payment processing; and device integration libraries for CGM (Dexcom API), continuous BP monitoring (Omron Connect), and pulse oximetry (Apple HealthKit and Google Health Connect). Every dependency is chosen because it is fit for healthcare -- not because it is the most popular option in a non-clinical context.

We connect the new system to your existing EHR (Epic, Cerner, Athenahealth, Meditech, or a smaller regional system), billing software (Kareo, AdvancedMD, DrChrono), scheduling tool (NexHealth, Acuity, or the scheduling module in your existing EHR), and third-party device feeds (CGM, BPM, pulse oximetry, connected scales). The integration approach is assessed per system during discovery: modern EHRs expose FHIR R4 APIs for bidirectional data exchange (patient demographics, appointments, clinical notes, results); older systems may require HL7 v2 message-based integration; scheduling systems typically have REST APIs with OAuth 2.0 authentication. No rip-and-replace: your staff continues using the systems they know, and the new platform adds capabilities on top of the existing stack without creating two parallel records for every patient. The integration is tested against a sandbox instance of your EHR before any production cutover, and the cutover is planned to avoid impacting clinical operations.

8 weeks of post-launch support is included in every fixed-price engagement. The support period covers: bug fixes for issues discovered under real patient load (load patterns in production are always different from what was tested in UAT); monitoring alert investigation and resolution via CloudWatch and Datadog; performance tuning if specific workflows are slower than target under real usage; on-call availability for critical issues during the first 30 days post-launch (the period when undetected edge cases in the clinical workflow most commonly surface); and a structured post-launch review at week 8 assessing system performance, error rates, and any clinical workflow adjustments the team has identified since go-live. Support does not expire at a calendar deadline if the system is still experiencing stability issues -- we stay until it runs reliably, not until the calendar says our obligation is fulfilled.

Build bespoke healthcare software designed around your specific clinical workflow rather than configured from a vendor template that was designed for a different care model. Custom development means the system records what your clinicians need to record, integrates with the systems your practice already uses, and can be modified as your workflow evolves without waiting for a vendor's product roadmap. Software types built: telehealth platforms (WebRTC video sessions, secure messaging, asynchronous consultation workflows), remote patient monitoring systems (device data ingestion, threshold alerting, provider dashboard), HIPAA-compliant EHR/EMR (encounter documentation, clinical notes, results management, prescribing workflows), chronic disease management platforms, medication adherence apps, and clinical decision support tools. Each build starts with a one-week discovery: clinical workflow mapping with the care team before any code is written.

Develop custom clinical and operational analytics dashboards that surface the KPIs your leadership and clinical teams need -- built from your actual data, not a vendor template that measures what the software vendor decides is important. Operational dashboards for practice managers: appointment fill rate, no-show rate, average wait time, revenue per visit, and collections efficiency. Clinical quality dashboards for medical directors: A1c control rates across diabetic patients, hypertension management compliance, preventive care screening completion rates, and readmission rates for post-discharge follow-up programmes. Population health views for value-based care contracts: patient panels stratified by risk score, care gap closure rates, and cost per member per month tracked against contract targets. All dashboards pull from your existing EHR, billing system, and RPM platform data without requiring manual export -- data refreshed daily or in near-real-time depending on the metric.

Replace legacy EHR systems, outdated patient portals, and desktop-only clinical software with modern, secure, maintainable platforms built on current technology -- without the clinical disruption of a hard cutover that turns off the old system before the new one has been validated in production. The modernisation approach uses a strangler fig pattern: the new system is built alongside the legacy system, clinical workflows are migrated one at a time starting with the lowest-risk and highest-benefit workflows, and the legacy system is decommissioned module by module as the new system proves itself under real clinical load. Data migration is planned and validated before any cutover: historical patient records, encounter notes, and clinical data migrated with reconciliation checks confirming record counts and critical field values match. The new system's tech stack (React, Next.js, Python, PostgreSQL, AWS) is maintainable by any competent engineering team, not a proprietary platform that creates a new vendor lock-in dependency.

Connect your new or existing platform to the clinical systems already in use: EHR integration via FHIR R4 (Epic App Orchard, Cerner FHIR Millennium, Athenahealth API) for patient demographics, appointments, clinical notes, results, and medication lists; e-prescribing via Surescripts API for electronic prescription routing to pharmacies with real-time formulary checking and drug interaction alerts; scheduling integration with NexHealth, Acuity, or the scheduling module in your EHR so appointments created in one system are visible in the other; billing integration with Kareo, AdvancedMD, or Waystar for automated charge capture from clinical encounter documentation; and telehealth provider integration (Twilio, Daily.co, Zoom Healthcare) for HIPAA-eligible video session infrastructure. Each integration is assessed for the correct API approach, authentication method, and data scope before development, and tested against the vendor's sandbox environment before production deployment. Data flows without manual re-entry: the nurse who documents the encounter does not also have to update a separate billing system, and the patient who books online does not have to re-enter their demographic information at check-in.

Reduce page load times, cut database query latency, and automate the manual workflow steps that slow clinical and administrative staff. We audit first -- profiling the application with production traffic patterns to identify the specific queries, API calls, and rendering paths that are causing the slowdowns -- before writing any optimisation code. Common findings: N+1 query problems in EHR data loading (where a patient list of 50 patients generates 50 individual database queries instead of one); missing database indexes on clinical record lookup queries; unoptimised FHIR bundle requests that fetch far more data than the view requires; and manual multi-step workflows (check appointment, pull patient record, open billing, update status) that can be combined into a single action. The audit produces a prioritised list of optimisations ranked by expected time savings per day for clinical staff -- so the work targets what actually costs time rather than pursuing technical elegance for its own sake. Performance targets set before optimisation starts; verified with load testing (k6 or Locust) under simulated clinical workload after changes are deployed.

Develop intelligent healthcare software using AI to address the clinical and operational problems where AI delivers measurable improvement -- not AI applied broadly because it is a current trend. Clinical AI use cases we build: risk stratification models that identify high-risk patients in a chronic disease population for proactive outreach (trained on your EHR data including diagnoses, lab results, medication history, and visit frequency); AI-assisted symptom triage chatbots that collect structured intake information before a telehealth appointment (reducing the clinician's documentation burden during the encounter); clinical decision support tools that surface relevant clinical guidelines and past patient context at the point of care using RAG over the practice's clinical knowledge base; and automated prior authorisation assistance that gathers and submits the required clinical documentation to payer systems with minimal clinical staff involvement. AI models deployed in healthcare require an audit trail (which model version made which recommendation, for which patient, at what time), human review for any AI output used in a clinical decision, and performance monitoring against outcomes data to detect model drift. These governance requirements are built in from the start, not added after.

Most healthcare software projects ship in 10--14 weeks from discovery to production. The timeline is achievable because discovery is a real week of work that produces a scoped, validated technical architecture before development begins -- not a pro-forma kickoff followed by scope creep that pushes the delivery date. Week 1: clinical workflow mapping, integration assessment, compliance architecture review, and wireframe validation with your care team. Weeks 2-12: bi-weekly sprint delivery with working software demonstrated at each sprint review. Week 13-14: UAT, HIPAA compliance review, and production deployment. The 10-14 week timeline applies to focused scope: a patient portal with EHR integration, a telehealth platform, or an RPM system. Platforms with multiple integrations and complex clinical workflows are scoped explicitly at the end of discovery so the timeline reflects the actual scope.

HIPAA compliance is designed into the architecture from the first technical decision, not added as a checklist item at the end of the build. Every system we ship includes: PHI encrypted at rest (AES-256) and in transit (TLS 1.3 minimum); multi-factor authentication for all clinical users with session timeout enforcement; role-based access control (RBAC) with the principle of least privilege -- nurses access patient records they are actively treating, not the entire patient database; immutable audit logs recording every PHI access event (who accessed which record, when, from which IP and device); Business Associate Agreements (BAAs) with every infrastructure provider that handles PHI (AWS, Twilio, Stripe); and a documented data flow diagram mapping where PHI is stored, transmitted, and processed -- the document your compliance officer needs for the HIPAA Security Rule review. HIPAA compliance review at the end of development and before production deployment verifies that all controls are implemented as designed.

We map your clinical workflows in Week 1 through structured sessions with the care team members who will use the software daily -- nurses, physicians, front desk staff, and billing coordinators -- because the workflow they describe is always different from what the practice manager assumes it to be. The workflow map documents every step in the current process, identifies where the friction is, and defines how the software should handle the clinical sequence. This is the document that drives the technical design -- if your nurses need to log RPM readings into the workflow without switching away from the patient encounter screen, that requirement is designed in before the database schema is finalised, not retrofitted after. The software fits how your practice actually operates. The outcome of Week 1 is a signed-off scope document and wireframes that your clinical leads have reviewed and approved -- development starts from a shared understanding of what is being built, not from a developer's interpretation of a brief.

Scope and cost locked before development starts in a written fixed-price proposal that defines the feature set, integration scope, compliance controls, and delivery timeline. No hourly billing, no estimates-that-are-actually-ranges, no "we'll know the real cost once we get into it." The price you approve in Week 1 is the price you pay at the end. Changes to scope -- features added, integrations expanded, clinical workflows modified after development starts -- are addressed via a change request process: the change is specified, sized, costed, and approved before any additional work begins. The fixed-price model aligns our incentives with yours: we scope carefully upfront because changes cost us as much as they cost you, and we have no financial motivation to expand scope after the project starts.

You see working software every two weeks in a live sprint demo with your clinical stakeholders present. Not slides, not screenshots -- the actual running system, demonstrated against real clinical scenarios by the engineer who built the feature. Problems that affect the clinical workflow surface at the Sprint 3 demo, when the fix is a day's work, not at the UAT review in Week 13 when the same problem would require rearchitecting a built system. Sprint demos include your clinical leads, practice manager, and any compliance stakeholders who need to review HIPAA controls -- because the people who can identify a clinical workflow misalignment are the people who use those workflows, not the project manager reviewing a feature checklist. Issues raised at each demo are logged, prioritised, and resolved in the next sprint before being re-reviewed at the following demo.

Slack access to your named project team throughout the engagement: the lead engineer, the project manager, and the QA lead -- not a support ticketing system or an account manager who relays messages. Weekly sync calls with a structured agenda (sprint progress, blockers, upcoming sprint plan, any risks or decisions needed from your side) and a written summary sent within 2 hours of the call so decisions and action items are documented. When a delay or risk surfaces -- an EHR vendor's FHIR sandbox is unavailable, a compliance review takes longer than estimated, a clinical workflow change from your side affects scope -- you hear about it the same day we know, not when the milestone delivery date passes. Healthcare software projects involve third-party dependencies (EHR vendors, certification bodies, compliance reviewers) that create schedule risk; early transparency about that risk is what allows it to be managed rather than absorbed as a silent delay.