This edition focuses on a practical question many enterprise teams face today: how to make AI, analytics, and data systems reliable enough to support growing operational demands.
As event volumes, data sources, and reporting needs grow, common issues emerge: unstable ingestion, slower dashboards, inconsistent metrics, orchestration overhead, and rising processing costs.
We explore how teams are operationalizing AI across engineering and platform environments. And how targeted infrastructure and data improvements help support those efforts through project examples, measurable outcomes, and insights from recent industry events.
AI initiatives become sustainable when teams can integrate them into everyday delivery processes and measure operational impact consistently.
Akvelon’s RAISE framework helps teams integrate AI into delivery environments more systematically – from governance and rollout to measurement and operational oversight.
Learn more on our homepage.
Where AI Creates Operational Value: Proof from Practice
The strongest AI gains usually appear in day-to-day engineering and operational work, helping teams deliver faster and manage growing system complexity.
A few examples from Akvelon projects:
- Large-scale codebase analysis: 4 days → 1 hour
Akvelon’s Agentic MCP approach reduced analysis time for a large brownfield enterprise system from 4 days to 1 hour by combining long-context analysis with auditable workflows.
- 80% reduction in manual API testing effort
An AI-assisted testing solution improved test creation and coverage while significantly reducing manual effort and accelerating release cycles.
Where Teams See the Most Operational Impact
The strongest results usually come from operational bottlenecks that teams already understand well: large-scale code analysis, testing overhead, reporting latency, infrastructure visibility, and coordination across complex delivery environments.
If your team is evaluating how AI fits into existing engineering and operational processes, we’d be glad to exchange perspectives – reach out.
Building Reliable Data Foundations for AI
As AI adoption expands across engineering and operational environments, infrastructure and data reliability quickly become foundational concerns.
Most analytics and AI initiatives eventually run into the same constraint: the data platform itself. In this post, we break down common bottlenecks and how to fix them using real project examples.
When ingestion is unstable, pipelines don’t scale, or data quality drifts, issues follow quickly. Analytics slows down, metrics become inconsistent, and AI systems lose accuracy. Teams spend more time fixing pipelines than building new capabilities.
Akvelon teams typically focus on strengthening ingestion, processing, validation, and observability layers so platforms remain stable as complexity grows.
Results from the Field
These are some of the operational challenges Akvelon teams helped clients address across complex data environments..
1. Scaling high-volume analytics without breaking pipelines
A multi-tenant analytics platform processing 35M+ events daily and more than 3TB of data began to hit scaling limits as usage grew. Akvelon redesigned ingestion, validation, and processing pipelines to support stable real-time and batch workloads at high volume. The redesigned platform improved observability, stabilized ingestion, and maintained consistent data quality under heavy load.
2. Reducing dashboard runtimes in a high-load analytics platform
As data volume increased, dashboards became slower and processing overhead grew in a high-volume advertising analytics platform. Akvelon reworked ETL pipelines and data models to improve efficiency and reduce processing load. Dashboard runtimes were reduced by up to 73%, while data processing volume decreased by up to 67%.
3. Improving data consistency across platforms
Event tracking became inconsistent across web and mobile environments in a cross-platform analytics and measurement system.
Akvelon standardized event schemas and validation processes to improve data quality and measurement consistency.
The changes improved measurement consistency across environments while increasing test coverage to 80%.
Scaling Data and ML Systems With Google Cloud
Once these foundational systems are in place, the next challenge becomes operational efficiency across pipelines, infrastructure, and ML workloads. In this post, we share how Akvelon works with Google teams to address these challenges across large-scale operational environments.
As a Google Partner, Akvelon contributes to systems built on Apache Beam, Cloud Composer, and GKE, helping teams improve pipeline stability, optimize Airflow orchestration, stabilize Kubernetes-based CI/CD environments, and reduce idle GPU costs for ML workloads.
We’re proud to contribute to this work alongside Google teams. For companies navigating similar challenges, this kind of operational experience becomes important when infrastructure complexity starts affecting cost, performance, and delivery speed.
Join Us at Upcoming Events!
Over the next few months, the Akvelon team will be attending several major technology and fintech events across Europe:
- Nordic Fintech Summit 2026 · May 20–22 · Helsinki, Finland
- Money20/20 Europe · June 2–4 · RAI Amsterdam, Netherlands
- VivaTech 2026 · June 17–20 · Paris Expo Porte de Versailles, Paris
If you’ll be attending any of these events, connect with Kirill Nesterenko, Akvelon’s Director of New Business Development, to continue the conversation in person.
Signals from Recent Industry Events
Akvelon team attended HANNOVER MESSE 2026 and Data & AI Warsaw Tech Summit 2026 this month. Across both events, discussions centered on the same operational questions engineering teams are now facing directly: infrastructure efficiency, observability, orchestration complexity, and AI integration into existing systems.
From HANNOVER MESSE 2026
In manufacturing and industrial environments, discussions are shifting from AI experimentation toward operational integration and reliability.
Key themes included:
- Making operational and real-time data AI-ready
- Integrating operational technology with enterprise systems and cloud platforms
- Embedding AI directly into industrial systems and production environments
From Data & AI Warsaw Tech Summit 2026
Discussions in Warsaw focused less on model benchmarks and more on infrastructure behavior, latency tradeoffs, observability, and operational cost.
Recurring topics:
- Observability becoming critical for cost control and system efficiency
- Combining GenAI, classical ML, and graph-based approaches in production systems
- Choosing models based on latency, reliability, and operational tradeoffs rather than benchmarks alone
- Several conversations also highlighted that organizational adoption remains harder than model experimentation itself.
These discussions reflect what we see more and more in client work: the surrounding operational architecture matters as much as the models themselves.
Keep up with the latest tech news, carefully curated and analyzed by Akvelon's experts.
- Google Cloud Expands Infrastructure for Multi-Agent Workloads: Google introduced updates to its AI Hypercomputer, including new TPU versions, data center architecture, and reinforcement learning capabilities.
For enterprise teams, it signals that infrastructure decisions are increasingly shaped by coordination overhead, inference cost, and workload orchestration requirements.
- Kubernetes and GKE Continue Expanding AI-Oriented Orchestration: Google shared updates to Kubernetes and GKE focused on supporting AI-native workloads and agent-based systems.
As AI workloads evolve, they become more dynamic and more dependent on multiple systems. Managing orchestration, latency, utilization, and cost becomes significantly more complex.
We’d love to hear which operational AI, infrastructure, or engineering topics you’d like us to explore in future editions. Email us on info@akvelon.com!