Services

Production AI and AWS engineering, without the generalist delivery model.

Start with an AI decision, a bounded system build, an engineering adoption problem, or a focused AWS production constraint. Every engagement stays senior-led and outcome-specific.

01Assess AI
02Build systems
03Adopt Claude Code
04Engineer AWS

AI engagements

Defined scope, senior accountability, usable artifacts.

Timing is typical rather than guaranteed. Scope is set after the technical scoping call and depends on integration depth, data readiness, and the risk profile of the workflow.

AI production assessment

Typical format · 1–2 weeks

For teams that need a defensible answer before committing to a build. We examine the use case, user workflow, data access, model behavior, eval design, risk, and AWS operating model.

Decision memo
Risk register
Eval specification
Architecture outline
Sequenced roadmap

Agentic system build

Typical format · 4–8 weeks

For a bounded workflow with a clear owner and measurable value. We build the application path, evaluation harness, AWS infrastructure, observability, and operational handoff together.

Production workflow
Evaluation harness
Infrastructure as code
Operating dashboard
Runbooks

Claude Code adoption

Typical format · 3–4 weeks

For engineering leaders who want adoption without unmanaged access, inconsistent repository context, or unclear review standards. The result is a working operating model, not a tool demo.

Maturity baseline
Repository standards
Permission boundaries
Workflow playbooks
Team enablement

AWS production engineering

Modernize the foundation your product depends on.

These services are available as focused standalone engagements or as part of AI delivery. The common thread is production engineering: explicit constraints, working infrastructure, operating evidence, and ownership transfer.

Serverless and event-driven systems

Design, build, or stabilize production workloads using Lambda, API Gateway, EventBridge, Step Functions, SQS, SNS, and infrastructure as code—with failure handling and observability built in.

Migration and modernization

Move applications and services to AWS with an explicit modernization path. We plan service boundaries, data movement, cutover, rollback, and the transition toward containers, serverless, or event-driven architecture.

FinOps and cost engineering

Connect AWS spend to architecture and product usage. We identify waste, establish unit economics, right-size workloads, and implement budgets, anomaly detection, and engineering guardrails.

AWS toolkit

Architecture chosen for the workload, not the trend.

We work across the AWS services needed to deliver and operate the outcome. Recommendations account for team capability, reliability, security, cost, and the burden of long-term ownership.

Lambda
API Gateway
EventBridge
Step Functions
SQS and SNS
ECS and Fargate
Amazon Bedrock
Identity and IAM
CloudWatch
Infrastructure as code
CI/CD
Cost controls
Well-Architected review

Scope discipline

A good engagement has boundaries before it has velocity.

Strong fit

A named business or engineering owner.
Access to representative data and subject matter experts.
A workload whose success and failure can be evaluated.
An AWS environment or a clear reason to build one.

Weak fit

An organization-wide transformation with no first workload.
A request to select technology before defining the outcome.
Staff augmentation with no technical ownership.
A proof of concept expected to bypass production controls.

Start with the problem

Start with the production decision that is currently blocked.

Share the workload, the AWS context, and the constraint. We will tell you directly whether an AI assessment, system build, Claude Code adoption, focused AWS engagement—or another team—is the right next step.

Discuss a project