Dear partners,
As we wrap up the year, we’d like to wish you a restful holiday season and a strong start to 2026.
In this December edition, we focus on one question: how do you turn AI into measurable outcomes in complex enterprise environments?
We share how our Agentic MCP Platform compressed days of work into hours, how enterprises are safely applying agentic workflows, and how predictive models and infrastructure engineering continue to elevate operational performance.
Thank you for staying connected with Akvelon. Happy holidays!
Akvelon’s Agentic MCP Platform for Enterprise-Scale Efficiency
The difficult part of applying AI in enterprise engineering isn’t the model - it’s the system: legacy code, scattered artifacts, and business logic spread across repos, tickets, and internal services. Our case study shows how Agentic AI tackles this problem in the real world.
Akvelon’s Agentic MCP Platform refines the architecture beneath the AI. By orchestrating specialized agents that share persistent context and execute tasks in parallel, it turns multi-step engineering work into one clean and validated output.
What this changed for a global client:
- 4 days → ~1 hour to analyze each directory of a 30 GB / 100k-artifact legacy repo
- Structured, searchable documentation + a unified system map
- A year-long review process projected to shrink to under two months
If you’re working with a big legacy codebase and a busy team, this approach can help you reduce complexity, accelerate delivery, and make your existing systems easier to work with.
AI Delivering ROI Across Real Client Projects
Within our latest AI Productivity Challenge, Akvelon gathered 80+ applied solutions, built within production environments. Explore flagship cases on our blog.
Here’s just some of the results our clients achieved with AI-powered automation:
- Repo documentation: 25–30 GB codebases analyzed in ~1 hour vs. ~4 days
- Faster feature delivery: Instruction Packs enable 35–60% quicker execution and higher first-pass success
- API config automation: ~75% faster generation & validation
- Engineering efficiency: 20% faster PR merges, 15% fewer review comments, ~10% better CI pass rates
- AI-ready code standards: usable AI-generated tests up 25% → ~60%
Teams are already seeing results from AI in practical and measurable ways.
Ready to apply these patterns to your team? Let’s talk.
Safe Agentic Automation in Real Enterprise Workflows
Agents promise to replace apps, but can they safely run business logic? We say yes, and in this case study, we show how Akvelon used an internal HR platform to test a safe, governed approach to introducing agent workflows without risking compliance or control.
Instead of adding more UI, we connected agents to enterprise data through a secure Intelligence Layer. HR managers operate in natural language while RBAC, validation rules, and governance run in the background.
Improvements from this approach:
- New workflows delivered in 1–2 weeks instead of 2–6 months.
- Process updates resolved in 1–2 days instead of weeks.
- No dedicated frontend required – business logic lives in governed prompts and rules.
- API-first, SQL-first, and MCP Intelligence approaches tested on live HR data.
While the testbed was HR, the core design (RBAC, validation, governance) is highly relevant to other enterprise workflows where control matters: analytics, CRM, forecasting, inventory, and any domain that needs governed, cross-system answers without rebuilding entire applications.
If you're evaluating safe ways to use agents in operations, we can walk you through the guardrails and architecture: reach out.
Engineering Spotlight: Smarter GPU Scheduling on GKE Autopilot
GPU workloads are hard to run efficiently: demand spikes, capacity isn’t always available, and overprovisioning quickly turns into idle spend.
Our DevOps team, led by Vlado Djerek, built a GKE Autopilot–compliant solution that optimizes GPU scheduling and eliminates idle resources, using Flyte + Kueue + Dynamic Workload Scheduler (DWS). It’s now featured on GKE AI Labs and shared with the Flyte open-source community → complete guide on Akvelon’s blog.
AI Coding: Strengths, Gaps, and the Evolving Developer Role
What happens when you ask AI to build a full app in a stack you’ve never used, and what still requires engineering judgment? Akvelon engineer Andrey Evstratov tested this by rebuilding the same app five times with AI assistance – here's his practical field report for anyone training junior developers or reviewing AI-generated code.
The takeaway: AI accelerates building, but architecture, error handling, and edge cases still require clear specs, verification, and disciplined engineering.
Case Study: Predicting Vessel Discharge Time for a Major Logistics Hub
What if every hour of delay could be predicted? In our detailed case study on Medium, we share how Akvelon used 2,200 historical port records to build a predictive ML model to estimate discharge duration before work begins.
The model explains 65% of discharge-time variability and improves scheduling accuracy by ~5 hours, reducing idle ships and cutting operational costs.
It also identifies the actual drivers of delays (containers, cranes, gangs), providing teams with evidence-based insights rather than guesswork.
If your operations depend on timing – whether in logistics, aviation, manufacturing, or any workflow with downstream SLAs – our approach demonstrates how ML can leverage historical data to prevent surprises and improve planning.
Future-Ready APIs for Integrated Enterprises
Extensible APIs are critical when dozens of systems must stay in sync—yet many integration layers don’t scale. In our new YouTube video, we share how Akvelon builds modern, reliable APIs that keep complex systems connected and fast.
Many companies still depend on API layers that can’t scale across WMS, tracking, billing, and other critical systems. This results in data silos, delays, and reduced visibility.
Akvelon develops APIs that eliminate these common issues. We’ve provided high-performance API solutions for clients such as Reddit, Looker, Microsoft, T-Mobile, and several Fortune 500 companies. Additionally, we enhance validation processes with our AI-powered API Testing Tool, which ensures reliability, security, and compliance at scale.
Our work helps enterprises achieve seamless integrations, more intelligent workflows, and infrastructure that’s ready for growth.
Akvelon Named One of the Top 5 Best Software Development Companies
We’re excited to celebrate a special milestone — Akvelon has been recognized as one of the Top 5 Best Software Development Companies in Bellevue, WA by Expertise.com.
This recognition highlights the dedication of our engineering teams and the meaningful results we deliver for clients across industries. We’re truly grateful for the trust, collaboration, and long-term partnerships that make achievements like this possible.
If you’re exploring a technology partner to build, modernize, or scale your systems, we’d be happy to support you.
Our 1-Hour Code Challenge As an Akvelon Tradition
Last week, our Dev Center hosted the third round of our 1-Hour Code Challenge, and it was the toughest yet.
One problem, sixty minutes, and no tool limits. Participants used everything from IDEs to ChatGPT and Gemini, racing to ship a correct solution under real pressure.
This mirrors our approach to real projects: AI accelerates the work, but engineers still handle the logic, edge cases, and final quality – the parts that matter.
Congrats to the winners, and thanks to everyone who joined. We’re already looking forward to the next round!
Keep up with the latest tech news, carefully curated and analyzed by Akvelon's experts.
- Google’s Gemini 3 and Antigravity: multimodal models and what they change for enterprise AI roadmaps.
Google’s latest release – Gemini 3 and the new Antigravity agentic development platform – pushes agentic workflows directly into tools used by millions. For enterprises, this signals that AI stops being “projects” and becomes product infrastructure, where architecture, data quality, and safe integration matter more than raw model power.
- Claude 4.5, Gemini 3 & More: what actually changes for your AI roadmap with each new model release?
Anthropic has released Claude Opus 4.5, its strongest model for coding, agents, and structured tool use — now available on AWS, Azure, and Google Cloud. It comes with upgrades like longer autonomous workflows, improved tool calling, and stronger spreadsheet and document handling.
For enterprises, the question isn’t “How powerful is the model?” but where it can reliably support governed, high-value use cases. That’s where architecture, data quality, and observability matter far more than benchmarks.
At Akvelon, we help teams turn new model capabilities into concrete roadmap decisions. We advise our clients on which workflows to automate, how to manage risk, and how to stay flexible across clouds and model providers. Reach out.