From AI integration to reliable production
I help CTOs and engineering teams take one stuck or launch-bound AI-enabled feature from integration to reliable production.
The focus is application architecture, authorized context, deployment, reliability, observability, latency, and cost—not an open-ended AI transformation.
13+ years in full-stack engineering, AWS, backend systems, APIs, cloud architecture, and production software delivery.
The gap
The blocker is often not the model alone. It is the surrounding product, data, API, deployment, and operating system that must work together under real constraints.
Where teams get stuck
Confidence drops when a feature needs real permissions, production integration, measurable release gates, and an operating model the team can support.
Services
Start with one feature, one production consequence, and evidence strong enough to make a release or investment decision.
The first paid engagement: a five-business-day Production Blocker Diagnostic. Implementation is proposed only when the evidence supports it.
Service 01
Confirm whether the problem, deadline, and decision justify a diagnostic.
A free 30-minute conversation for a CTO or technical owner with one identifiable AI-enabled feature, a production consequence, and a plausible diagnostic budget.
First paid engagement
Service 02
Find controlling blockers and leave with a scoped technical release recommendation.
A five-business-day, EUR 6,000 engagement for one stuck or launch-bound AI-enabled feature. The output is evidence, measurable release gates, and a bounded remediation plan.
Service 03
Implement the bounded controls required to pass agreed release gates.
A three-to-four-week implementation engagement offered only after a paid diagnostic or equivalent evidence has produced a controlled backlog.
How I think
Clear engineering judgment is useful only when it produces testable evidence. These principles guide the diagnostic without pretending every feature has the same blocker.
I first look at the business decision the AI is supposed to improve. If the use case is vague, the build usually becomes expensive noise.
Production AI systems need boundaries: what data can be used, what actions can be taken, and what should happen when confidence is low or a dependency fails.
Release evidence, observability, and human review matter more than clever prompting. Teams need to reproduce failures and defend a go/no-go decision.
Model usage, caching, rate limits, and fallback behavior should be deliberate. Cost per successful outcome is an engineering concern, not an afterthought.
Why work with me
You are working with someone who understands product delivery, backend architecture, cloud systems, and the operational realities that decide whether a feature can ship safely and be supported after launch.
Core stack & focus areas
I am not only an AI experimenter. I come from 13+ years of full-stack and backend engineering — AWS, APIs, CI/CD, deployment, and real delivery constraints.
No buzzword decks. I focus on what is worth building, what is feasible in your timeline, and how to ship it without breaking your existing product.
Fit
Clear fit signals help the right clients self-select. This work is best for teams that care about reliability, integration, and product quality, not just quick demo output.
Good fit
Not the right fit
Process
A diagnostic-first approach that avoids an open-ended transformation and makes the next engineering decision explicit.
Start here
Verify the feature, production consequence, deadline, technical owner, and budget path.
Review the system, constraints, traces, tests, and client-approved evidence relevant to the blocker.
Identify controlling blockers, measurable gates, one bounded architecture decision, and prioritized remediation.
Implement a bounded backlog, verify the agreed gates, and hand over operating evidence.
FAQ
One identifiable AI-enabled feature that is stuck or approaching launch, with a technical owner and a production, customer, or commercial consequence within 90 days.
The five-business-day Production Blocker Diagnostic produces blocker evidence, current failure modes, measurable release gates, a prioritized remediation backlog, one bounded architecture decision, a rough implementation range, and a scoped executive recommendation for the reviewed domains.
EUR 6,000, paid before kickoff. The scope is one named workflow, up to two domains for deep analysis, and up to three failure scenarios for reproduction. Implementation is not included.
Yes, when the diagnostic or equivalent evidence produces a bounded backlog. A production-hardening sprint is scoped separately with explicit acceptance and change control.
The diagnostic can examine application and integration architecture, authorized context, deployment, rollback, reliability, observability, latency, or cost, but no more than two domains are investigated deeply. Specialist AI security, formal model evaluation, and advanced retrieval or agent audits require separately validated capability or a named, disclosed specialist.
English-operating B2B SaaS teams, typically with 30–120 employees, where a CTO, VP Engineering, or technical owner needs a scoped technical production recommendation for one AI-enabled feature.
With a free 30-minute fit triage covering the feature, current state, deadline, consequence, technical owner, and budget path. It is not a free architecture review.
Yes. The diagnostic is designed to produce evidence and a backlog your team can execute. Any implementation support, team involvement, and access boundaries are disclosed and agreed before work begins.
Latest thinking
Primary-source research and engineering analysis focused on permissions, authorized context, tool scope, and information exposure.
Production AI Security Series · Part 2
A relevant vector-search result is not automatically authorized. This Production AI Security Series article explains how tenant, role, and ACL scope should constrain retrieval before any chunk reaches model context—and how to test that boundary with denied callers, traces, and downstream tools.
Read “Retrieval Boundaries: Permission-Aware RAG”Contact
If one AI-enabled feature is stuck between integration and production, send the feature, current state, deadline, and consequence of not shipping.
A short message is enough. You do not need a polished brief, but there should be a real feature, technical owner, and production decision.
Germany / Remote-first · Typically responds within 1–2 business days