Principal AI Architect | AI Transformation Leader
Andrew Gordieiev
Principal AI Architect who builds agentic systems inside the product: from architecture to running in production, with quality and governance built in.
Principal Solution Architect, Head of Architecture Department and one of the leadership ambassadors driving a 300+ engineer company's AI-Native transformation. 3+ years building LLM-based systems; 20 years in IT, 15+ in production software, 8+ leading architecture.
- Ad agency · Live client platform · Financial-services clientsTurned a live CEO prototype only one person understood into an AI product the agency's clients useThe agency's platform for its financial-services clients depended on one person. Rather than freeze it, I bet on AI agents changing it under checks the client owns, so nothing untested reaches clients. Roadmap never paused; clients now see how their brands show up in four LLMs' answers and use an AI assistant.Open the case
- Leadership · 300+ engineers · Every roleCo-leading a 300+ engineer company's bet on AI-Native work in every role, developer to back-officeOne of the leadership ambassadors: co-authored the strategy and set one standard for AI patterns, governance and evaluation, so the company scales AI instead of improvising it team by team. Together, the ambassador group launched ~20 AI-Native production projects.Open the case
- Pharma · Regulated · A growing AI and data portfolioLead architect of a pharma company's AI and data portfolio: faster patient replies, faster management, one standard for every numberOne rule everywhere: AI drafts and explains, people and deterministic code decide. Patient replies in clinical operations go out faster, none unapproved; management moves faster with an LLM app I architected and audited that drafts agenda-tied minutes, decisions and action items for a person to review; every number has one approved definition; and C-level deck assembly is ~70% faster.Open the case
- Logistics · 5,000+ people · AI guardrails frameworkBrought AI to 150-200 developers and testers inside an enterprise's hard security lines; vetted AI changes became their default way to shipNo code or data could leave the perimeter, no unvetted change could land. My call: agents propose, an automated score screens every change, a person approves. It held both lines, went to an AI enablement team for wider rollout and cut dev time ~30%.Open the case
- Agentic SDLC · Dark factory · Idea to live productBuilt a dark factory that takes a product from idea to production with agents in every role: ~40 releases in 6 monthsOne rule: machine gates, not people, decide what ships. Agents carry the whole development cycle with no human sign-off: they write the requirements, design, architecture, code and tests, review each other, run manual QA on the built app just as a person would and ship to production. ~40 production releases in 6 months.Open the case
- Media · New platform from scratch · Discovery to MVPRan AI-Native discovery for a brand-new media platform replacing one that could no longer scale, and laid the foundation several new teams built the MVP onThe old platform could no longer scale or grow, so a new one was built from scratch. I ran its discovery AI-Native: BAs, designers and dev leads worked from one AI-built knowledge base, and I laid the agent setup and specs every new team starts from. I set the team composition and hired the tech leads; their teams took the new platform to MVP under my governance.Open the case
Work
Selected cases
Each case is one strategic call on AI and what it changed. Open a node for the outcome, the decision behind it and how it is enforced.
Accountable for architecture and AI · bet on AI agents changing the live product, not a freeze
From a Prototype Only One Person Understood to an AI Product Clients Use, with No Feature Freeze
Ad agency · Live client platform · Financial-services clients
- Roadmap
- Never stopped while the product was hardened
- AI in production
- How brands show up in four LLMs' answers, an AI assistant, LLM insights for paid media and SEO
An ad agency's B2B platform for financial-services clients was live as a CEO-built prototype that only one person understood. The brief: production grade without stopping the roadmap. Rather than freeze features, I bet on AI agents changing the live product, and set its test strategy, covering the code agents write, the AI features themselves and drift in the models behind them, so nothing reaches production until it passes those tests. On that base I am accountable for the AI features clients now use: how their brands show up in four LLMs' answers, an AI assistant, and LLM insights for paid media and SEO.
Situation
- The client keeps every test and gate, even without us
- AI features tested like code: only tested changes reach clients
Approach
How I keep AI correct where a mistake is expensive
Put the gate where a mistake is expensive.
How much an agent does alone is set by what a mistake would cost. Where a mistake is cheap to undo, machine gates are enough: a pipeline with no human sign-offs shipped ~40 production releases in 6 months. Where it would reach a patient, an executive's number or a customer's order, a person decides: AI drafts patient replies but cannot send one, explains C-level numbers but cannot change one, fills transport orders but never submits one.
Make the product safe for agents to change, without stopping the business.
A live product only one person understands is usually frozen for a rewrite while clients wait. I made the opposite call: AI agents keep shipping the roadmap while they harden the product. To make that safe, I pin today's behaviour in tests first, map every production incident to the check that closes its whole class and build checks in that order, and test the code agents write, the AI features themselves and drift in their models. That is how a CEO-built prototype became an AI product clients use, with the roadmap never paused.
One standard before any framework.
Left alone, every team improvises its own way to build with AI, and each fails differently. Before any framework, I set one engineering standard for a 300+ engineer company: shared architecture patterns, governance for safety, hallucinations and cost, and one way to evaluate quality. That is how the company scales AI instead of improvising it team by team. As one of its leadership ambassadors I contributed at different levels to all ~20 AI-Native production projects the group launched, and started 5 of them myself.
Measure before claiming.
An AI claim without a number is an opinion. Every model is benchmarked and scored before it is chosen, cost is tracked per model, every AI comic page is similarity-scored to keep character drift under 10%, and SPACE + DORA across 300+ engineers show the classic J-curve: an early dip, then a climb above the baseline.