Technical Capabilities
Technology Stack
We are technology-pragmatic, not technology-opinionated. We use what is appropriate for the problem — and we have the depth to work across the full range of modern distributed systems tooling.
01 — Architecture
Backend engineering with precision and depth.
Our backend engineering capability centers on Go and Java for high-throughput service development, with Python applied selectively in data processing and AI pipeline contexts. We use Go where concurrency, binary size, and startup time matter — financial services, real-time APIs, WebSocket backends, and blockchain node integrations where resource efficiency is a hard constraint. Java and the Spring ecosystem are applied where operational maturity, library depth, and long-term maintainability are the primary concerns.
For service communication, we design with synchronous REST and gRPC, asynchronous event-driven patterns using Apache Kafka, and persistent WebSocket connections for real-time bidirectional communication. WebSocket infrastructure is designed with horizontal scalability at the connection layer, Redis pub/sub for message fanout across nodes, and fine-tuned connection lifecycle management for systems handling tens of thousands of concurrent clients.
Data layer design reflects the access patterns and consistency requirements of each service: PostgreSQL for transactional workloads, Redis for caching and pub/sub, Elasticsearch for search, ClickHouse for analytical workloads, and purpose-built blockchain indexers for on-chain data that must be queryable at API speed.
02 — Cloud & Infrastructure
Cloud-native infrastructure across AWS, GCP, and Azure.
We architect and operate cloud infrastructure across all three major providers. AWS is our primary platform for most enterprise and FinTech engagements — EKS for Kubernetes workloads, RDS and Aurora for managed databases, MSK for managed Kafka, and CloudFront with WAF for edge and security. GCP is preferred when AI/ML workloads require tight integration with Vertex AI or BigQuery at scale. Azure is applied in enterprise environments where Active Directory integration and Microsoft ecosystem compatibility are requirements.
Kubernetes is the default deployment target for all microservice workloads. We design cluster architectures with multi-tenancy, namespace isolation, resource quotas, and pod security policies from day one. Workload autoscaling is implemented at both the pod and node level, with KEDA for event-driven autoscaling based on queue depth or custom metrics.
All infrastructure is codified in Terraform, with modular designs that allow environment parity between development, staging, and production. CI/CD pipelines are built in GitHub Actions or GitLab CI, with ArgoCD or Flux for GitOps-based continuous delivery. Observability stacks are built on the Prometheus/Grafana/Loki/Tempo combination for self-hosted deployments, or Datadog where managed observability is preferred.
03 — AI, Blockchain & Data
AI, on-chain systems, and data pipelines built for production.
Our AI engineering capability covers the full stack from data pipeline to inference endpoint. For LLM integration, we maintain working depth across OpenAI, Anthropic, and Mistral APIs, as well as open-weight models deployed via vLLM and HuggingFace TGI on GPU infrastructure. Model selection is based on latency, cost, and capability requirements — not vendor preference. We build RAG systems, AI agent architectures, and multi-modal pipelines that are observable and tunable in production.
On the blockchain side, we design and build smart contracts on EVM-compatible chains (Solidity), integrate with Solana programs, and implement hybrid on-chain/off-chain architectures. For DeFi and NFT platforms, we build high-performance blockchain indexers using The Graph or custom subgraphs, wallet connection infrastructure (WalletConnect, MetaMask SDK), and off-chain computation layers that maintain verifiable integrity.
For data pipelines at scale, we use Apache Flink and Kafka Streams for stateful stream processing, dbt for SQL-based data transformation, and Apache Airflow or Prefect for workflow orchestration. All AI and data systems include evaluation pipelines, quality metrics, and observability — not just in development, but in production.
The right technology for your actual constraints.
Tell us what you are building. We'll explain exactly what we would use, why, and what the tradeoffs are — before any engagement begins.