Askona — Enterprise Architecture and AI Transformation

Role: architecture/engineering leadership in CTO scope | Span: 20+ teams, 40+ services

$680M+: revenue
15M: customers
9000+: employees
40+: services

Led a controlled digital and AI transformation in a high-stakes enterprise environment to speed up delivery, improve reliability, and institutionalize architecture standards without breaking business continuity. A core part of the work was a multi-party integration layer — a shared fabric for many independent owners (teams, hybrid cloud and on-prem, partner footprints) so integration seams stayed explicit and contracts did not drift.

Role & authority

Position: Enterprise Architect (working within the CTO's organization)
Reported to: the CTO/CIO
Scope: Enterprise architecture and AI-focused transformation; coordinated 20+ distributed teams across 40+ services.
Organization scale (influence, not direct reports): $680M revenue, 15M customers, 9000+ employees. Askona is the largest mattress & furniture retailer in Eastern Europe (15M customers).

Recommendations

From Askona colleagues (LinkedIn)

"Grigoriy consistently demonstrated exceptional technical leadership and deep expertise as an Enterprise Architect. He was one of the key drivers behind the adoption of event-driven architecture and microservices across Askona's IT landscape — pragmatic, collaborative, and always focused on the right solution for both business and technology."

Aleksander Zinger · Director-level IT Business Partner, Askona

"I highly recommend him as an exceptionally competent professional in Enterprise Architecture, Solution Architecture, AI-driven innovation, and technical leadership. He led the company's strategic AI initiative, driving the adoption of AI tools and practices across the organization."

Ilya Chernov · Head of PMO IT, Askona

Situation

• Context: large enterprise (15M customers, 9000+ employees, $680M+ revenue).
• Architecture landscape: hybrid cloud + on-prem, 40+ services, high integration complexity.
• Business pressure: faster change delivery with no compromise in stability.
• Critical risk: conservative teams, low business engagement, and over-ambitious change scope.
• Marketplace-style framing: delivery depended on a multi-party integration layer—event buses, APIs, and governance as the coordination fabric across independently owned services and teams, not “just an AI project.”

Task

• Increase release cadence and time-to-market without production instability.
• Reduce delivery friction across teams and systems.
• Modernize legacy under controlled governance.
• Move AI from demos into real operating workflows.
• Keep integration seams predictable with many system owners: shared exchange rules and boundaries so changes in one part of the landscape do not break adjacent systems.

Action

• Coordinated transformation across 20+ distributed teams under one architecture frame.
• Built event-driven integration (Kafka/RabbitMQ/API) as the backbone between independent teams and services—explicit contracts at ownership boundaries, faster launches, less integration friction.
• Implemented platform governance and high-availability standards.
• Shifted execution to phased rollout instead of a big-bang rewrite.
• Led AI seminars, workshops, and 1:1 enablement with teams and managers.
• Introduced practical RAG, AI-assisted coding, and AI use cases for delivery and business decisions.

Skills applied

• Enterprise architecture and coordination of 20+ distributed teams across 40+ services.
• Multi-party integration layer: aligning many owners, contracts, and operating models in one production footprint.
• Event-driven integration (Kafka, RabbitMQ, APIs) to reduce integration friction.
• Platform governance, high availability, and phased legacy modernization without a “big bang.”
• AI enablement: RAG, AI-assisted / vibe coding, and applied AI patterns within security constraints.
• AI leadership: seminars, workshops, and 1:1 guidance for teams and management.
• Transformation management with a strong focus on business continuity and production stability.

Result

Operational impact

stable gains visible in first 2 years

Cultural impact

by year 3-4 teams defended new standards

Architecture impact

event-driven principles became company standard

Org impact

AI practices became the foundation of a dedicated AI function

Strategic outcome

Release cadence and time-to-market accelerated, platform reliability increased, cross-team friction decreased, and bottleneck visibility improved. Integration seams between parts of the landscape became more predictable—fewer one-off manual alignments for every change. Under my leadership, AI became the primary operating pattern across all levels — from management decisions to engineering execution.

Public Askona business context from open sources: 2023 revenue under RAS was RUB 49.12B (vs RUB 40.94B in 2022), and for key legal entity "TD Askona" open 2024 reports mention RUB 41B revenue and RUB 1.2B net profit. These figures describe company-level performance and are not a direct attribution to this transformation alone.

Practical examples

• AI bot over data lake: business questions in natural language were translated into SQL, with both human-readable and machine-readable outputs.
• Next best offer: I personally designed and trained an ML model on historical order data for recommendations in cart and email scenarios.
• Order lifecycle timestamp tracker: implemented end-to-end event tracking to surface bottlenecks and delay zones in order processing.
• Georesolving proxy microservice: as part of AI-assisted coding training sessions, built a service that converts coordinates to address and address to coordinates for integration needs.
• AI-agent demo stand: assembled a cross-organization setup to demonstrate collaborative specification alignment between agents and reduce manual coordination friction.
• Training scale: delivered dozens of sessions on engineering practices, management, and applied AI for audiences of 100-200 participants.

Personal contribution

• Institutionalized event-driven integration and phased transformation without a big-bang rewrite—in practice the multi-party integration layer for 40+ services and 20+ teams with separate owners.
• Personally delivered the key AI/ML assets: data-lake AI bot, next best offer ML model, timestamp tracker, georesolving proxy service, and AI-agent demo stand.
• Scaled adoption through recurring enablement for teams and managers, turning AI-assisted coding into an operating practice rather than a one-off demo.

Case Materials

• youtube: YouTube channel
• distributed asynchronous systems course: YouTube playlist
• blog (ru): dobryakov.com/blog

Grigoriy Dobryakov — AI-Driven Head of Engineering

$680M+ revenue 40+ services, 20+ teams

Connect as MCP Email me