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Alexander Polomodov · expert consulting

Make the decision.
Understand the consequences.

I help CTOs and engineering leaders make decisions about AI, architecture and engineering organizations. We examine your situation, compare alternatives and document a recommendation, risks and the next step.

Discuss your company's challengeHow we work together

Experience as a CTO and Technical Fellow at a large fintech. My work connects architecture, engineering management and AI in software development.

When to bring me in

Where to go next with AI in development

Tools are already in use, but it is unclear what to scale, what to own and how to assess the impact.

What we examine
One workflow: stack options, data requirements, agent permissions, quality evaluation and the full cost of the result.
What you take away
A map of options and ownership, a recommendation for a limited pilot, and criteria for continuing or stopping.
Discuss a review like this

Related material: Agent Stack Map

How to assess an architecture decision

A platform or architecture change is ahead. The team has options, but the cost of getting it wrong and the consequences are unclear.

What we examine
A specific decision: constraints, alternatives, the cost of complexity, and the implications for teams and operations.
What you take away
An independent recommendation, a risk map and assumptions to validate before committing resources.
Discuss a review like this

Related material: Channel → Product → Platform

Why a growing team is not delivering faster

More people and tools are in place, yet delivering changes remains slow or unpredictable.

What we examine
A selected workflow, team interactions, ownership boundaries and the usefulness of current metrics.
What you take away
A map of likely bottlenecks, prioritized changes and a way to test their effect on your process.
Discuss a review like this

Related material: Engineering Productivity

Start with one decision

The first paid engagement is an expert review of an agreed question, with a written recommendation. Its scope depends on the problem and the material available.

  1. Agree on the question

    You describe the context. In an introductory conversation, we establish how I can help, what material is needed, who should participate and what the outcome should be.

  2. Examine the situation

    I study the agreed material. In a working session, we test assumptions, compare alternatives and discuss their consequences.

  3. Document the recommendation

    You receive a written conclusion, alternatives, risks, open questions and the next step to validate. We discuss clarifications and the limits of the recommendation.

Agree on scope and fee before work begins

After the brief, we agree on scope, participants, timing, the deliverable and a fixed fee for that stage. The introductory conversation establishes scope; substantive diagnosis is part of the paid engagement.

When the decision needs ongoing support

We can continue through a pilot or a series of connected decisions. We separately agree on the period, questions, meetings and scope of written support. As evidence arrives, we revisit recommendations and document what should change.

What the result looks like

The document should help your team make a decision and explain it to colleagues. Here is an abbreviated example of a recommendation.

Illustrative example · fictional situation and data

Should AI tools be rolled out to the entire team?

Context
A team uses AI to draft tests. Users report saving time, but review and rework costs are not yet included.
Options
Expand licenses to the whole team; test one workflow in a limited pilot; invest in a custom agent.
Recommendation
Start with test drafting in one repository. Compare work with and without AI on comparable tasks. Decide on expansion after evaluating the complete process.
Main risk
Faster generation may increase review time. The number of generated tests alone does not demonstrate quality or savings.
What to validate
First-pass acceptance, review and rework time, missed defects and the full cost per accepted task. Agree on success and stopping criteria before the pilot.
Next step
Assign an owner, select comparable tasks, establish a baseline and define access limits. Then run the pilot and revisit the expansion decision.

In an actual engagement, the recommendation is grounded in the company's data and constraints. Measurable impact is validated through the team's subsequent work.

Explore my approach in advance

These materials show how I frame questions, work with constraints and evaluate alternatives.

From tool cost to the cost of an outcome

How to account for review, rework and failures when evaluating the economics of AI in development.

Read the AI economics analysis

From platform complexity to team decisions

How to discuss the need for a platform through ownership, constraints and maintenance cost.

Read the platform complexity analysis

What decision is your team facing?

Message me on Telegram or by email. A short description is enough to start discussing your situation and a suitable engagement.

Message on TelegramOpen an email with the briefalex@polomodov.tech

Include in your first message

  1. Your role, company and team context
  2. The decision to make or the problem to examine
  3. Why it matters now and what you have already tried
  4. Your preferred timing and who participates in the decision

If a budget is already set, include it so we can agree on a suitable scope.

Next, we clarify the question and agree on a proposal: scope, deliverable, timing and fee.