We plan, build, and operate production systems across cloud, AI, security, data, and custom software. Each service below can be delivered as a standalone engagement or as part of an integrated transformation program. Our approach is composable: discovery → quick wins → scale-up → handover, with measurable outcomes at every stage.
A successful cloud program begins with crisp boundaries and shared vocabulary. We define landing zones, identity strategy, network segmentation, and cost policies before migrations start. Our platform engineering teams provision golden paths—self-service templates and paved roads—so product squads can deploy safely without waiting on tickets. Observability, SLOs, and incident workflows are baked in from day zero.
We tailor to multi-cloud realities: portability for critical workloads, native services where they make sense, and a clear total-cost-of-ownership model. Beyond lift-and-shift, we prioritize modernization—adopting managed databases, serverless patterns, and container orchestration to compress lead times and reduce toil. Cost controls include budgets, anomaly alerts, usage heatmaps, and reserved-instance planning.
AI is only valuable when it is dependable. We build retrieval-augmented generation (RAG) systems with robust evaluation harnesses, incorporating semantic search, chunking strategies, and prompt safety checks to ensure answers are grounded in your data. For predictive workloads, we establish end-to-end ML pipelines—feature stores, experiment tracking, model versioning, canary deployments, and continuous evaluation with human-in-the-loop review.
Governance matters: data lineage, access controls, red-teaming, and model cards are first-class deliverables. We provide fallback rules when confidence is low, and instrument user feedback loops to keep models honest. Our copilots are designed for explainability and task boundaries, integrating with enterprise systems through audit-ready adapters.
Security must be continuous. We implement zero-trust principles (identity-centric controls, micro-segmentation, device posture), hardened baselines, secret management, and continuous compliance. Our red teams pressure-test assumptions with offensive testing; our blue teams build defense-in-depth with telemetry pipelines, detection engineering, and response automation. We align with NIST, ISO 27001, SOC 2, and sector requirements.
Resilience is broader than security: chaos experiments validate failover plans and capacity; backup and restore drills ensure RPO/RTO targets are real; tabletop exercises prepare leaders for the fog of incidents. We formalize service levels and error budgets so teams can balance feature delivery with reliability without guesswork.
Data work succeeds when governance and usability travel together. We architect lakehouse or warehouse topologies based on your access patterns, then design ingestion, quality gates, and semantic layers that make analytics fast and trustworthy. Metrics are defined once, reused everywhere, and versioned—no more shadow spreadsheets. We integrate BI tools and build notebooks and APIs so analysts and applications can consume data safely.
We build maintainable software: clean boundaries, observable behavior, and pipelines that catch defects early. Our architectures emphasize evolvability—domain-driven design, event streaming where appropriate, and pragmatic use of microservices. Toolchains are standardized: IDE configs, linting, tests, and release automation so teams move quickly without breaking foundations.
Sustainable change is a people project. We provide executive briefings, architecture clinics, and hands-on training tailored to your stack. Our enablement squads pair with your engineers to embed practices—SRE, DevEx, threat modeling—so improvements persist after we leave. We measure adoption with lead-time, change failure rate, and service health, turning culture goals into observable metrics.