Technologies We Master

Our primary frontend framework. We use React for complex SPAs, data-heavy dashboards, and multi-step user flows. We follow compound component patterns, custom hooks, and React Query for server state.

Used for all SEO-sensitive products, marketing sites, and full-stack apps that need server-side rendering. App Router, Server Components, ISR, and edge functions are our default approach.

TypeScript is mandatory on every project. Strict mode enabled, full type coverage, Zod schemas for runtime validation. It eliminates entire classes of bugs before they reach production.

Vue 3 with Composition API and Pinia for state management. Preferred for teams already on Vue, internal tooling, and projects where a lighter bundle size matters.

Default CSS strategy on all new projects. Combined with shadcn/ui for accessible component libraries and Framer Motion for animations. Design tokens enforced via Tailwind config.

Every project starts in Figma. We deliver complete design systems with variables, components, and interactive prototypes before writing a line of code. Dev Mode handoff is built in.

Used for large enterprise applications where Angular's opinionated structure provides consistency across big teams. Angular 17+ with standalone components and signals.

SvelteKit for performance-critical marketing sites and embeddable widgets. Compiles to vanilla JS with zero runtime overhead, ideal when bundle size and Time to Interactive are critical KPIs.

Our primary backend runtime. We use Fastify for high-performance APIs, NestJS for enterprise applications requiring strong architecture, and Express for simple services. Always TypeScript.

Primary language for all data engineering, ML pipelines, and scientific computing. FastAPI for async REST APIs. Django for feature-rich admin-backed applications. Celery for task queues.

Used for high-throughput microservices where latency and CPU efficiency are critical. Payment processing engines, real-time event processors, and CLI tooling. Goroutines for concurrency.

Apollo Server and Federation for complex multi-service APIs. Used when clients need flexible queries and we want to avoid over-fetching. Schema-first design with codegen for full type safety end-to-end.

Event streaming backbone for high-volume applications. Used for decoupling microservices, real-time fraud detection, audit trails, and analytics pipelines. Managed via Confluent Cloud on AWS.

Real-time bidirectional communication for live dashboards, collaborative tools, chat applications, and trading platforms. Socket.IO for broad compatibility, native WS for performance-critical services.

Spring Boot for enterprise-grade backend systems in finance and healthcare. Used when working with existing Java codebases or clients with Java-first mandates. Quarkus for cloud-native Java.

OpenAPI 3 spec-first design. Every API ships with versioning, rate limiting, authentication (OAuth2/JWT), pagination, and full Swagger documentation. Postman collections provided on delivery.

Default relational database for all projects. We use Prisma or Drizzle ORM, write complex queries with window functions, build audit trail triggers, and manage schema migrations with zero downtime.

Document database for content management, user profile stores, and IoT metadata. Atlas Search for full-text search. Change streams for real-time sync. Aggregation pipelines for complex reporting.

In-memory caching, session storage, rate limiting, pub/sub messaging, and distributed locks. Redis Streams for lightweight event sourcing. Used on every production deployment to reduce database load.

Full-text search, log aggregation (ELK stack), and analytics. Used for e-commerce search, log monitoring dashboards, and security event analysis. Managed via Elastic Cloud on AWS.

Time-series extension for PostgreSQL. Used for IoT sensor data, financial tick data, and application metrics. Continuous aggregates provide near-instant query performance on billions of rows.

Cloud data warehouse for business intelligence and analytics. dbt for data transformation, Fivetran for ELT pipelines. Used for companies that need to query terabytes of structured business data.

AWS-native NoSQL for serverless applications, gaming leaderboards, and session management at scale. Single-table design patterns, DynamoDB Streams for event-driven workflows.

Vector database for AI-powered similarity search. Used in RAG pipelines, semantic search engines, and recommendation systems. Paired with OpenAI embeddings for enterprise knowledge bases.

Primary cloud platform. We are AWS Advanced Partners. Core services: ECS, EKS, Lambda, RDS Aurora, ElastiCache, CloudFront, Route53, SQS, SNS, S3. Infrastructure defined with Terraform and CDK.

Every service ships as a Docker container. Multi-stage builds for minimal image sizes, Docker Compose for local development, and integration test environments that mirror production exactly.

EKS on AWS for production orchestration. Helm charts for application packaging, Karpenter for node autoscaling, Argo CD for GitOps deployments. HPA and KEDA for demand-based scaling.

All infrastructure defined as code. Modular Terraform with remote state in S3, drift detection, and policy-as-code with Sentinel. Every environment: dev, staging, prod.. is reproduced from the same codebase.

CI/CD pipelines for every project. Automated test suites, SAST scanning, Docker builds, Terraform plan and apply, and zero-downtime blue/green deployments on every merge to main.

APM, infrastructure monitoring, log management, and RUM for production systems. Custom dashboards, SLO tracking, and PagerDuty integration for on-call alerting. Every deployment is monitored from day one.

GKE, Cloud Run, BigQuery, Vertex AI, and Firebase. Preferred for ML-heavy workloads and clients with existing Google Workspace infrastructure. Certified GCP architects on staff.

AKS, Azure Functions, Cosmos DB, and Azure OpenAI Service. Used for clients in regulated industries with Microsoft enterprise agreements. Active Directory integration and Entra ID for enterprise SSO.

GPT-4o and o1 for complex reasoning, embeddings for semantic search, DALL-E for image generation, Whisper for transcription. We build production-grade AI features with proper fallbacks, rate limiting, and cost controls.

Orchestration layer for complex LLM workflows. RAG pipelines, multi-step agents, tool use, and memory management. LangSmith for observability and prompt testing in production.

Custom model training for classification, regression, and anomaly detection tasks. Fine-tuning pre-trained models with LoRA for domain-specific use cases. ONNX export for edge deployment.

Classical ML for fraud detection, churn prediction, customer segmentation, and pricing models. Full MLflow experiment tracking, model registry, and automated retraining pipelines.

Default choice for cross-platform mobile. Expo SDK with EAS Build for streamlined deployment. React Navigation, Zustand for state, and React Query for data fetching. Custom native modules when needed.

Native iOS development with SwiftUI and UIKit. Used for apps requiring deep iOS integrations such as HealthKit, ARKit, Core Motion, and Bluetooth. Full App Store submission and review management.

Native Android with Jetpack Compose. Coroutines for async, Room for local database, WorkManager for background tasks. Used for Android-specific features like NFC, hardware sensors, and Google Pay integration.

Used when pixel-perfect custom UI is required across iOS, Android, and web simultaneously. Dart language, Riverpod for state management, and Freezed for immutable data models.