Technology Stack
The Right Stack for the Job
Technology-agnostic by conviction, production-hardened by experience. We choose the tools that fit your constraints—not ours.
Cloud Platforms
Multi-Cloud Expertise
Deep, production-proven experience across all three major cloud providers—so we can meet you where you are.
Amazon Web Services
Deep expertise across the full AWS ecosystem—from SageMaker and Bedrock for AI/ML to EKS, Lambda, and the complete data analytics stack.
Microsoft Azure
Enterprise-grade deployments leveraging Azure's AI services, Databricks integration, and hybrid cloud capabilities for regulated industries.
Google Cloud Platform
Leveraging Google's strengths in data analytics, Vertex AI, and BigQuery for organizations that demand cutting-edge ML infrastructure.
Core Technologies
Battle-Tested Tools We Trust
Every technology in our repertoire has been validated in production under real-world pressure.
AI & MLOps
End-to-end machine learning lifecycle—from experimentation to production serving and monitoring.
Data Engineering
Scalable data pipelines and platforms that turn raw data into reliable, queryable assets.
Infrastructure
Cloud-native infrastructure that scales reliably and deploys repeatably across environments.
Application
Modern application frameworks and data stores for building performant, maintainable systems.
Architecture Patterns
Proven Architectural Approaches
We don't force-fit patterns. We select the architecture that best serves your scale, team, and business goals.
Microservices
Decomposed, independently deployable services with clear domain boundaries and well-defined APIs.
Event-Driven
Asynchronous, loosely coupled architectures using message brokers and event streams for real-time responsiveness.
Serverless
Function-as-a-service and managed services that eliminate infrastructure overhead and scale to zero.
Data Mesh
Domain-oriented, decentralized data ownership with federated governance and self-serve data platforms.
Lakehouse
Unified analytics combining the flexibility of data lakes with the reliability and performance of data warehouses.
ML Platform
End-to-end machine learning platforms with feature stores, model registries, and automated retraining pipelines.
Our Philosophy
How We Choose Technologies
We don't chase trends or default to what's popular. Every technology recommendation is grounded in your specific constraints—team expertise, existing infrastructure, compliance requirements, and timeline.
The result is a stack that your team can own and evolve long after our engagement ends. No vendor lock-in, no unnecessary complexity, no resume-driven development.
Evaluate Constraints First
Team skills, compliance needs, existing infra, and timeline all shape the recommendation before any technology is considered.
Favor Boring Technology
Proven tools with strong ecosystems and community support reduce risk. We reach for cutting-edge only when the use case demands it.
Optimize for Ownership
We select tools your team can maintain and extend independently—because the best system is one you can run without us.
Integration
Connecting with Your Ecosystem
New systems don't exist in a vacuum. We specialize in integrating with your existing tools, data sources, and workflows.
Data Sources
- REST APIs
- JDBC/ODBC
- Streaming
- File-based
Auth & Identity
- SSO/SAML
- OAuth 2.0
- LDAP/AD
- RBAC
Observability
- Datadog
- Splunk
- PagerDuty
- OpenTelemetry
CI/CD
- GitHub Actions
- GitLab CI
- Jenkins
- ArgoCD
Let's Talk Architecture
Tell us about your technical landscape and we'll recommend the stack and architecture that fits your goals.