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Vadim Polyakov

Service

AI Strategy

A practical AI strategy: where AI creates value in your business, the architecture and guardrails to deliver it, and a portfolio of use cases with ROI.

Is this you?

Signs it’s the right time

  • Dozens of AI pilots, none in production
  • Leadership wants an AI strategy by next quarter
  • Concerns about data privacy, security and model risk
  • Unclear build-versus-buy decisions on AI tooling

Outcomes

What changes for your organization

01

A prioritized use-case portfolio

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02

A secure AI platform architecture

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03

Governance that enables, not blocks

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04

First use cases in production

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Approach

How the work is structured

A proven sequence that keeps leadership aligned and produces something useful at every step.

  1. 1

    Explore

    Weeks 1–2

    Executive workshops and use-case discovery across the business.

  2. 2

    Prioritize

    Weeks 3–4

    Value and feasibility scoring, data readiness and risk review.

  3. 3

    Architect

    Weeks 5–6

    Platform architecture, guardrails and operating model.

  4. 4

    Prove

    Optional

    Lead one or two use cases from pilot into production.

Deliverables

Tangible artifacts you keep

Practical, decision-ready documents your team will use long after the engagement ends.

  • 01AI opportunity assessment and use-case portfolio
  • 02Data readiness review
  • 03Reference architecture for AI workloads
  • 04Responsible AI and governance framework
  • 05Build / buy / partner recommendations
  • 06Pilot-to-production plan

Vadim gave our board a technology strategy it could actually understand and our engineers a roadmap they believed in. Within two quarters we shipped faster with a smaller cloud bill.

Alex MorganCEO, Northwind Logistics

FAQ

Common questions

Do we need a data platform first?

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How do you handle AI risk and compliance?

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Can you train our team?

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