AI automation consulting and hands-on implementation for founders and small teams. I start with one bounded business outcome, then make the workflow useful, observable, and maintainable.
Already have an agent or automation that works in a demo but fails in real use? That is a valid starting point.
The unit of work is one concrete operating outcome with a bounded scope.
Up to two hours to map the current workflow, failure modes, data access, tool permissions, and production risks. The output is a scoped recommendation: proceed, narrow the system, or stop. Any deeper review is scoped separately.
Build or harden one workflow or agent system. Add the controls that demos skip: approvals, retries, fallbacks, logs, cost limits, and a clear handoff.
Review failures and costs, tune the workflow, maintain integrations, and keep the system useful as tools, prompts, data, and business rules change.
AI implementation services are most useful when there is an owner, a repeatable workflow, and a result you can inspect.
Connect the trigger, source data, model, business rules, approval points, and destination. Useful examples include research preparation, support assistance, reporting, structured review, and internal handoffs.
Define tool access, state, stop conditions, human review, evaluation cases, failure handling, logs, and cost limits. The goal is a controlled system with bounded autonomy.
Turn a specific set of company sources into a grounded internal knowledge system, then connect it to one executable workflow. Source ownership and update rules matter more than the label.
Connect models to the systems they need without giving them unlimited access. Add observability so a person can understand what ran, what failed, what it cost, and what to do next.
Separate release problem? App Store, Google Play, mobile packaging, and production release work for an existing app belong at App Release Ops. This page is for the behavior, tools, data, reliability, and operation of AI systems.
My perspective comes from building and operating products, including the parts that appear after the first successful demo.
Built across approximately seven years, spanning AI apps, marketplaces, creator tools, growth, and infrastructure.
A production Stable Diffusion API launched in approximately 48 hours on serverless AWS. It is an operating example, not a delivery-time promise.
Hypee was acquired by Yandex. AI Boost, an AI photo and video editor, exited in 2025 for approximately $450,000.
I still write production code. The diagnostic and implementation are grounded in the same constraints: small teams, imperfect data, limited attention, and systems that need an owner after launch.
A small first step protects both sides from a vague build with no reliable definition of done.
Send that context. I will tell you whether the $299 diagnostic is the right next step.