Discovery & Scoping
A structured discovery sprint to define requirements, constraints, success metrics, and technical architecture before writing a line of code.
Architecture Design
We produce a detailed technical spec covering data model, API design, ML pipeline, infrastructure, and integration points.
Iterative Build
2-week sprints with regular demos, feedback integration, and continuous deployment to a staging environment from the start.
Testing & Hardening
Load testing, security review, edge case coverage, and MLOps setup before any production traffic.
Deployment & Support
Production deployment with monitoring, alerting, and on-call support. Post-launch retainer available for ongoing development.
- Full-stack AI application development (web, mobile, API)
- ML model design, training, and deployment
- Real-time data pipelines and streaming architecture
- Cloud-native infrastructure (AWS, GCP, Azure)
- MLOps setup: monitoring, retraining, rollback
- REST and GraphQL API design and documentation
- Security-first engineering with penetration testing
- Scalable architecture from day one — no rewrites later
See all case studiesThey shipped a production ML system in 6 weeks that our internal team estimated would take 6 months. And it actually worked at scale.
What tech stack do you use?
We are stack-flexible and choose technologies based on your requirements. Common choices include Python/FastAPI for ML backends, Next.js for frontends, and AWS or GCP for infrastructure. We integrate with whatever you already run.
Do you do fixed-price or time-and-materials?
Discovery and scoping phases are fixed-price. Build phases are typically structured as fixed-price milestones tied to functional deliverables, not hours — so you always know what you're getting.
What happens after launch?
We offer post-launch retainer agreements covering monitoring, model maintenance, feature development, and on-call support. Handoff to your internal team with full documentation is also available.
Can you work with our existing engineering team?
Yes. We frequently operate as an embedded AI team alongside existing engineering organizations, handling the ML and AI components while your team manages the product layer.