Software consulting services

Independent software consulting for U.S. teams that need production-ready web applications, reliable AWS infrastructure, practical AI integration, technical due diligence, or platform modernization — without adding a permanent headcount. Engagements are scoped around outcomes, constraints, and clear decision points.

What kinds of consulting engagements are available?

Engagements can cover a defined product or feature build, a technical assessment, AI or cloud modernization work, contact-center and voice AI delivery, or ongoing engineering advisory support. Scope, milestones, and decision points are agreed before delivery begins so stakeholders know what will ship and when to reassess.

How does a consulting project begin?

The process starts with the business goal, current constraints, and the outcome you need. From there, I define a practical scope, identify technical risks, recommend a delivery plan, and clarify what success looks like for engineering, product, and leadership stakeholders.

Who are these services designed for?

These services are designed for U.S. teams that need senior full-stack, cloud, or AI engineering without adding a permanent role. I work remotely from Kentucky and collaborate with product managers, engineers, security owners, and executives who need clear architecture and reliable delivery.

What full-stack application work do you take on?

Typical full-stack work includes React and TypeScript frontends, NestJS or Node.js APIs, multi-tenant data models with isolation guarantees, GraphQL or REST contracts, and greenfield builds or feature delivery on existing production codebases with attention to performance and maintainability.

What AWS and cloud architecture services are included?

Cloud work covers serverless-first design, infrastructure as code, private networking, secrets management, IAM least privilege, CI/CD, observability, and cost-aware architecture using services such as Lambda, API Gateway, DynamoDB, S3, CloudFront, Cognito, SQS, and Step Functions.

How do you approach AI and LLM integration?

AI engagements focus on production usefulness: provider abstraction, model routing and fallbacks, retrieval-augmented generation pipelines, NLP workflows, evaluation and guardrails, and—when privacy requires it—in-browser inference so sensitive data never leaves the client.

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