How we work
A structured, five-phase approach from discovery through launch and beyond. Typical engagement: 8-16 weeks, depending on scope.
Discovery
Architecture
Engineering
Deployment
Optimisation
01
Discovery
Duration: 1–2 weeks
We begin by understanding your business, market position, and technical requirements.
We do
- Stakeholder interviews (2–3 sessions)
- Workflow mapping and process analysis
- Technical audit of existing systems
- Market and compliance requirements review
You do
- Participate in 2–3 interviews
- Provide access to existing systems
- Share initial vision and constraints
You get
- Technical Requirements Document (TRD)
- Architecture Decision Record (ADR)
- Project scope and timeline proposal
- Risk assessment
For AcademicGrid, Discovery revealed that no two institutions manage admissions the same way — admissions processes vary wildly between K-12 schools and universities. This insight shaped the configurable workflow engine at the platform's core.
02
Architecture
Duration: 1–2 weeks
System design, data modelling, and UI/UX prototyping before build begins.
We do
- System architecture and data modelling
- API contract definition
- UI/UX prototypes in Figma
- Infrastructure planning
- Security and scalability review
You do
- Reviews and approves architecture docs
- Provides feedback on UI/UX prototypes
- Clarifies infrastructure preferences
You get
- System architecture diagram (C4 model)
- Entity Relationship Diagram (ERD)
- Figma prototypes (all user-facing flows)
- Infrastructure specification
- Security and compliance checklist
For MediCoreHMS, the architecture phase designed a microservices-based system with separate modules for patient records, scheduling, clinical workflows, and staff management. Each module was architected for HIPAA compliance and real-time data sync across hospital departments. The design supports everything from a 50-bed clinic to a 500+ bed hospital network on the same codebase.
03
Engineering
Duration: 4–12 weeks (varies by scope)
Agile development in 2-week sprints with bi-weekly demos and code reviews.
We do
- Agile sprints (2-week cycles)
- Full-stack development (frontend, backend, database)
- Code review and quality assurance
- CI/CD pipeline setup and testing
- Regular sprint demos every 2 weeks
You do
- Sprint reviews every 2 weeks
- Feedback on demos and prototypes
- Access to staging environment for testing
- Sign-off on completed features
You get
- Working software in staging environment
- Sprint summaries and velocity reports
- Test coverage reports
- Technical documentation
- Regular sprint demo recordings
Hypercompile's engineering phase took 10 weeks across 5 sprints. Sprint 1-2 built the core LLM integration framework. Sprint 3-4 added vector database integration and prompt engineering tools. Sprint 5 completed workflow automation and enterprise API design. By week 7, customers could integrate their first AI models. By launch, 4 production deployments were running.
04
Deployment
Duration: 1 week
Production setup, data migration, performance testing, and launch preparation.
We do
- Production infrastructure setup (AWS/GCP/Azure)
- Database migration and validation
- Performance testing under load
- Monitoring, alerting, and logging setup
- Runbook and incident response documentation
You do
- Approves go-live decision
- Coordinates internal communications
- Provides production data samples
- Participates in go-live war room
You get
- Live production system
- Runbook documentation
- Monitoring dashboards (uptime, performance, errors)
- Incident response guide
- On-call support for first 2 weeks
MediCorePay's deployment required compliance auditing for healthcare payment processing. We deployed to a dedicated AWS environment with encryption at rest, PCI-DSS configuration, and HIPAA logging. The system went live with 8 clinics on day one. Pre-launch load testing ensured it handled 500+ concurrent billing transactions. Monitoring dashboards tracked transaction success rates, payment gateway health, and compliance audit logs from day one.
05
Optimisation
Duration: Ongoing
Monthly performance reviews, feature prioritisation, and continuous improvement.
We do
- Monthly performance and usage analysis
- Feature prioritisation based on data
- Scaling adjustments (database, caching, API)
- User feedback integration
- Security patches and updates
You do
- Regular check-in cadence (monthly or quarterly)
- Feedback on new features
- Priority decisions for roadmap
You get
- Monthly health reports
- Quarterly feature roadmap updates
- SLA-backed uptime commitments
- Performance optimization recommendations
BakeStock launched with early access to 15 bakeries in India. Post-launch optimization involved: 2x growth in 3 months requiring database indexing and query optimization; integration of supplier APIs based on customer feedback; bulk import tools for inventory migration; and a mobile app for field staff. Monthly usage reports showed 40% improvement in order processing time and 60% reduction in manual data entry.
Ready to start?
We typically recommend starting with a Discovery phase (1–2 weeks) to align on scope and build a detailed roadmap.
Get in Touch