Vadim Vladymtsev CTO · StackLevel Group

Build. Scale.Create impact.

Enterprise AI products for regulated operations.

Product, architecture and technical judgment. From the first decision to governed production.

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Vadim Vladymtsev, Enterprise AI CTO
Enterprise AI Technical diligence CTO advisory

Selected work.

Explore the work

Century Private enterprise AI

Private AI.
Operational control.

Century brings assistants, workflows, governed data access, audit, observability and deployment control into one enterprise product surface.

Explore Century
◎ CenturyPlatform architecture

THE OPERATING LAYER

Controlled work.

AssistantsWorkflowsAPIs
Governed accessPolicies · Roles · Audit
ModelsCompany dataInfrastructure
Private deployment boundary
Illustrative architecture · based on the published product capabilities

Where I can help.

Decisions. Systems. Delivery.

Regulated organisations

Enterprise AI

When a priority AI use case needs architecture, controls, ownership and a credible path beyond pilot stage.

Scope a governed deployment

Investors

Technical diligence

When an AI company’s product, data, architecture, team and deployment claims need technical assessment before a decision.

Review the diligence framework

Founders and executives

CTO advisory

When product, architecture, roadmap and engineering delivery have diverged and a CTO-level diagnosis is needed.

Request a CTO diagnostic

The way I work

A practical approach
to complex challenges.

Product and architecture for governed enterprise AI.

About my approach
  1. 01

    Understand the context.

    The decision, the constraints and the people involved.

  2. 02

    Design the right system.

    Product, data access, controls and architecture together.

  3. 03

    Connect decisions to delivery.

    Align ownership, engineering and the operating model.

  4. 04

    Make production governable.

    Evaluation, adoption and observability — not just a pilot.

Current mandate & responsibilities

I’m Vadim Vladymtsev, CTO at StackLevel Group. I work across product, architecture and engineering to help enterprise AI initiatives move from use-case selection to governed production.

Enterprise AI becomes useful only when the product, data access, controls, ownership and delivery system work together. My focus is the operating layer around the model: what the system is allowed to do, how it is evaluated, how users adopt it, and how it remains observable in production.

Scope a governed AI deployment →

Essays.

All essays

Essay · 2 November 2026

Revising an Architecture I Designed

What rebuilding part of my own microservices application taught me about engineering responsibility, migration costs, and professional trust.

Essay · 29 October 2026

Who Gets the Time AI Saves?

AI can make a task faster. The value depends on who receives the time saved and what the company enables them to do with it.

Essay · 22 October 2026

Shared Understanding Takes Work

What coaching a competitive programming team taught me about explaining ideas, sharing context, and helping strong people work independently.

Let’s talk

Let’s build a more
capable future.

Start with the decision that needs to be made.

Share the organisation, current constraint, decision timeline and stakeholders involved. Do not include confidential data in the first message.

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