PRACTICAL AI ENGINEERING

Your AI pilot works. Now make the system dependable.

I help teams fix unreliable agents, connect business software, and run private AI infrastructure. We start with the failure or bottleneck you can show, then agree a scope, a test, and who will maintain the result.

Describe the problem →

Engineering help, scoped to your problem.

AI Agent Architecture & Deployment

Design tool boundaries, orchestration, evaluation and deployment for agents that work with your actual systems.

Scope & deliverables ↗

Business Automation & Integrations

Connect APIs, databases and business workflows with monitoring, recovery and human approval where it matters.

Scope & deliverables ↗

Local & Private AI Deployments

Evaluate local and self-hosted models against your hardware, data handling and workload requirements.

Scope & deliverables ↗

AI Reliability & Security Reviews

Review tool failures, permission boundaries, retries, observability and the risks around production workflows.

Scope & deliverables ↗

Technical SEO & AI-Assisted Engineering

Improve crawlability, site architecture, metadata and repeatable SEO checks through engineering.

Scope & deliverables ↗

Custom Software & AI Integration

Build or extend software with APIs, agent workflows and automation that fit the product and its users.

Scope & deliverables ↗

From problem to working system.

Understand the problem

Identify the workflow, owner and outcome that matters.

Assess existing systems

Review your tools, data boundaries and constraints.

Define technical scope

Agree deliverables, success criteria and exclusions.

Prototype or validate

Test the highest-risk assumptions before expanding.

Implement and test

Build the integration and verify failure behavior.

Deploy and document

Prepare monitoring, ownership and a maintainable handover.

Evaluate the work before the pitch.

Explore my public projects, read the engineering notes, and use the free resources. Each project is labeled according to its evidence and publication status.

Tell me what you're building.

Share your workflow, current tools and preferred timeline. A consultation starts with whether the project is a useful fit.

What problem should we solve?

Tell me what’s happening and what you’d like to change. Software, AI agents, infrastructure, security—start anywhere.

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What budget are you working with?

A rough range helps me suggest a sensible scope. It’s fine if you’re still figuring it out.

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Where can I reply?

Just your name and email. We can discuss the rest later.

Your name

Email address

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Questions before we start

Can you work with our existing tools?

The first step is reviewing their APIs, permissions and data constraints. Integrations are scoped after that review.

Can the system run privately?

Local or self-hosted options can be evaluated against your workload and hardware. The right architecture depends on requirements and test results.

How do you define success?

We agree task-specific acceptance criteria before implementation, including failure recovery and human intervention.