Services
Ten practice areas covering the full lifecycle of a cloud platform and the data and AI workloads that run on it, from the first landing zone to AI agents in production. Every engagement is led by a senior engineer and ends with your team owning the result.
Cloud foundations and landing zones
A governed, multi-account AWS estate, built entirely as code, that is ready for audit from the first day.
Every workload you run inherits the quality of its foundation. We design and build multi-account AWS environments with centralized identity, segmented networking, preventive guardrails, and organization-wide logging, all defined in code and delivered with an operational handoff package.
Business outcomes
- New, compliant accounts provisioned in hours rather than weeks
- Preventive controls that stop misconfiguration before it reaches production
- One auditable source of truth for every environment
Typical deliverables
- AWS Organizations, OU, and service control policy design
- Identity federation and least-privilege access model
- Hub-and-spoke network baseline with private connectivity
- Account vending pipeline in Terraform
- Centralized CloudTrail, Config, and security findings
- Operational handoff package and runbooks
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Technologies
Kubernetes platform engineering
Production-grade internal platforms on Amazon EKS, with golden paths for developers and guardrails for security teams.
We build Kubernetes platforms that application teams can adopt without becoming Kubernetes experts. Clusters are reproducible from code, tenants are isolated, delivery runs through GitOps, and upgrades follow a schedule instead of an incident.
Business outcomes
- Faster, self-service delivery for application teams
- Predictable, low-risk cluster upgrades
- Compute that scales with demand and cost that follows it down
Typical deliverables
- EKS reference architecture across environments and regions
- Multi-tenancy model with namespace, network, and access isolation
- GitOps delivery with controlled promotion between environments
- Karpenter autoscaling and capacity strategy
- Gateway API ingress and multi-cluster networking with Cilium
- Upgrade playbooks and platform runbooks
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Technologies
Cloud security and continuous compliance
Controls enforced automatically and evidence produced automatically, so audits confirm what is already true.
Compliance programs fail when controls live in spreadsheets. We implement security as part of the platform: enforced policy, centralized audit trails, protected sensitive data, and runtime detection, with evidence collected continuously for your auditors and security office.
Business outcomes
- Audit evidence that exists before the auditor asks
- Sensitive data kept out of logs, analytics, and lower environments
- Reduced exposure from misconfiguration and over-privileged access
Typical deliverables
- Control matrix mapped to your target framework
- Threat model for critical systems
- WAF, IAM, KMS, and network hardening
- Tamper-evident, centralized audit logging
- PII masking for logs and non-production data
- Policy as code and runtime threat detection with alert routing
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Technologies
DevSecOps and software supply chain
Release pipelines that make the secure path the fastest path, from commit to production.
We build delivery pipelines that test, scan, sign, and promote every change with a clear approval trail. Security checks run automatically, so teams ship more often with less risk and change control has the evidence it needs.
Business outcomes
- Higher release frequency with lower change-failure rates
- A verifiable record of what was deployed, by whom, and from which source
- Vulnerabilities caught in the pipeline rather than in production
Typical deliverables
- CI/CD pipelines with environment promotion and approvals
- Container image scanning, signing, and SBOM generation
- Secrets management integrated with cloud KMS
- Self-hosted, autoscaling CI runners on Kubernetes
- Infrastructure as code with automated policy checks
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Technologies
Site reliability engineering and observability
Reliability measured against service-level objectives your business agrees to, not anecdotes.
We define service-level objectives with your business owners, instrument systems end to end, and build the alerting, incident process, and recovery testing that keep those objectives met.
Business outcomes
- Incidents detected by monitoring, not by customers
- Shorter time to diagnose and recover
- Disaster recovery that has been tested, not assumed
Typical deliverables
- SLO and error-budget definitions with business owners
- OpenTelemetry instrumentation for metrics, logs, and traces
- Actionable alerting and on-call runbooks
- Incident response process and post-incident reviews
- Backup, recovery, and failover testing
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Technologies
Cloud cost optimization (FinOps)
Unit-cost visibility and sustained savings, without slowing delivery.
We make cloud spend visible by team, product, and environment, then reduce it through rightsizing, autoscaling, scheduling, and commitment planning, with guardrails so savings persist after we leave.
Business outcomes
- Every dollar of spend attributed to an owner
- Lower run-rate without degrading performance
- Budget forecasts that finance can rely on
Typical deliverables
- Tagging and cost allocation model
- Rightsizing and Kubernetes bin-packing improvements
- Savings Plans and commitment strategy
- Showback dashboards and budget alerts
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Technologies
Data engineering and analytics platforms
Governed pipelines and lakehouses that deliver trusted, current data to analytics and AI.
Reporting, analytics, and AI are only as reliable as the data beneath them. We build data platforms on AWS in which ingestion, transformation, and orchestration are defined in code, quality checks run on every load, and the catalog shows where each dataset came from and who may use it.
Business outcomes
- Reports, dashboards, and models built on data that is tested and current
- Pipeline failures and stale data detected before they affect a report or a decision
- Sensitive data classified, masked, and access-controlled wherever it flows
Typical deliverables
- Data platform architecture: lakehouse, warehouse, and streaming
- Batch and streaming ingestion, including change data capture
- Version-controlled transformations with automated tests
- Data quality checks, freshness monitoring, and alerting
- Data catalog, lineage, and fine-grained access control
- Orchestration, cost controls, and operations runbooks
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Technologies
Machine learning and MLOps
Predictive models run as production systems: reproducible, reviewed, and monitored for drift.
A model that performs well in an experiment is not yet a system the business can depend on. We build the pipelines around it: versioned data and features, automated training and validation, approval before release, and monitoring that shows when a model no longer fits the data it sees.
Business outcomes
- Models released through the same reviewed, auditable path as application code
- Every prediction traceable to a model version and the data it was trained on
- Model drift detected by monitoring and corrected by controlled retraining
Typical deliverables
- Use-case assessment, success metrics, and baseline models
- Feature pipelines and a governed feature store
- Automated training and validation, with a model registry
- Real-time and batch inference on Amazon SageMaker AI or Amazon EKS
- Drift, performance, and bias monitoring with retraining triggers
- Model documentation for risk and audit review
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Technologies
Applied AI and LLM operations
Production AI on governed infrastructure: private, evaluated, observable, and cost-controlled.
We take AI from pilot to production responsibly. Assistants answer from your approved sources and cite them, every interaction is logged, quality is measured against your own test questions, and sensitive outputs go to a person for review.
Business outcomes
- Staff find answers in policies, contracts, and case files in seconds
- Every answer traceable to its source
- Documented evaluation and risk assessment before wider rollout
Typical deliverables
- AI use-case triage and risk assessment
- Retrieval-augmented generation architecture on your cloud
- Evaluation harness built from your real questions
- Guardrails, audit logging, and human review workflows
- Model gateway with usage and cost controls
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Technologies
Agentic AI development and operations
AI agents that work across your systems, with scoped access and human oversight.
An agent that can take action carries more risk than an assistant that only answers. We build each agent with narrowly scoped tools, isolated execution, a person approving consequential actions, and evaluation against real tasks before and after release. Every step an agent takes is recorded.
Business outcomes
- Routine multi-step work completed by agents, with staff reviewing the exceptions
- Every agent action authorized, logged, and attributable
- Agent quality and cost measured on your own tasks, not on vendor benchmarks
Typical deliverables
- Agent use-case selection and risk assessment
- Agent and multi-agent workflow architecture
- Tool integrations through the Model Context Protocol
- Least-privilege identity, sandboxed execution, and approval gates
- Task-based evaluation suite with regression testing
- Tracing, audit logging, and cost controls for agent runs
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Technologies
Engagement models
Programs scoped to your environment, obligations, and outcomes. Each begins with a discovery phase that sets scope, team, and success criteria, and ends with a documented transfer of ownership. California public agencies can engage us through small business purchasing methods for qualifying scopes. See our certification.
Cloud Strategy and Platform Assessment
An executive and engineering assessment of your cloud estate against the AWS Well-Architected Framework and your regulatory obligations, producing a target-state architecture and an investment roadmap leadership can fund.
Key deliverables- Executive readout
- Risk register
- Target-state architecture
- Prioritized modernization roadmap
Enterprise Landing Zone and Cloud Foundation
A multi-account, multi-region AWS foundation with centralized identity, segmented networking, preventive guardrails, and organization-wide logging, delivered entirely as code with full ownership transferred to your team.
Key deliverables- Landing zone as code
- Account vending pipeline
- Guardrail and access model
- Operations runbooks
Kubernetes Platform Modernization
An enterprise internal developer platform on Amazon EKS spanning environments and regions, with GitOps delivery, tenant isolation, policy guardrails, and observability, including migration from retired components such as Ingress NGINX to Gateway API.
Key deliverables- Platform reference architecture
- Multi-cluster EKS platform
- Golden paths for application teams
- Platform operating model
Application Modernization and Migration
Portfolio-level migration of legacy and virtualized workloads to containers and managed AWS services, executed in planned waves with clear cutover, rollback, and validation criteria.
Key deliverables- Portfolio assessment and wave plan
- Migrated workloads
- Cutover and rollback runbooks
- Decommissioning plan
Security and Compliance Program Enablement
Technical control implementation and continuous evidence collection for SOC 2, HIPAA, PCI DSS, NIST SP 800-53, or California state security requirements, from gap analysis through audit support.
Key deliverables- Control matrix and gap analysis
- Implemented technical controls
- Automated evidence pipeline
- Audit support
Reliability and Resilience Engineering
A service-level objective program, end-to-end observability, and tested disaster recovery for business-critical systems, including multi-region failover where availability requirements demand it.
Key deliverables- SLOs and error budgets
- Observability stack
- Incident response process
- Tested recovery and failover
Cloud Financial Management Program
Cost governance that gives finance and engineering a shared view of spend: allocation by product and team, unit economics, commitment strategy, and engineering changes that reduce run-rate without degrading service.
Key deliverables- Allocation and showback model
- Unit cost metrics
- Commitment strategy
- Implemented optimizations
Enterprise AI Platform and Governance
A governed foundation for generative AI on your own cloud, with a model gateway, retrieval over approved data sources, evaluation, guardrails, and audit trails, plus the first production use cases and an AI governance framework.
Key deliverables- AI platform architecture
- Production use cases
- Evaluation and monitoring
- Governance aligned to NIST AI RMF
Scope your engagement
Tell us your objectives and constraints. A senior engineer replies within one business day.