46% of cancer cases are diagnosed too late, often at advanced stages. That’s why early detection is critical. Here – akvelon.com/healthcare-ai-… we describe how we helped solve this problem and others. Reach out to bring meaningful change to your healthcare solutions! #healthTech

Akvelon is heading to #Next25! We're excited to share that our Director of New Business Development, Ashley Pikle, will attend #GoogleCloudNext at Mandalay Bay in Las Vegas! Heading to Vegas? Send Ashley a DM to connect:

Open-Source or Proprietary AI? The choice is becoming increasingly relevant to long-term AI planning. There’s no one-size-fits-all approach - only the right fit for specific goals. See a detailed comparison and key strategic insights:

Most businesses are overpaying for cloud—without knowing it. At Akvelon, we’ve helped F5, Akoya Biosciences & others cut costs with DevOps, testing infra, & cloud-agnostic design. Let’s talk about how we can make your cloud work smarter.

In a recent interview, Microsoft’s CEO predicted that apps, as we know them, will disappear and replaced by AI agents directly interacting with databases. In future posts, we’ll share our insights, lessons learned, and practical patterns you can apply.

Instead of letting LLMs interact directly with your database, have them operate through APIs you already trust. We tested 8 ERP operations and achieved an 89% success rate, with safe fallbacks and native observability. API-first agents lead to quicker adoption and reduced risk.

Let's build what's next. Akvelon delivers: - Reliable systems at scale - AI-driven platforms that speed up ops - Solutions for complex engineering challenges Chosen by leaders like Google and Microsoft.

From one project to 5+ years of partnership. Microsoft Azure’s Michael Lu shares how our work grew from UI dev to ML & AI engineering. Watch the testimonial: youtube.com/watch?v=fd11XZ… #AIEngineering

Can AI agents replace enterprise apps? We tested two extremes: APIs vs SQL. What we found: Each solves part of the problem. Each breaks in different places. The key question isn’t the interface, it’s how agents understand business logic.

If agents are to replace apps, they must do real work, not just answer prompts. We tested an agent on real HR data with direct SQL access. What we learned: raw SQL works for simple tasks. Complex operations need context.

AI impact is real when it’s part of the workflow. Across client projects, we’ve applied AI in 80+ production use cases. This is what we achieved using AI in production. • Reviews: days → ~1 hour • Builds: +35–60% faster • PRs: ~20% faster merges • Tests: 25% → ~60% usable

What if we predict vessel delays before they lead to increased costs? We built an ML model that forecasts vessel discharge time from real terminal data: • 2,200+ events • ~5h mean error • Planning-grade visibility for ops teams Read the case study: shorturl.at/AcOk8

Agents promise to replace apps, but can they run business logic safely? We tested this on a live HR platform. Result: governed workflows delivered in weeks, not months, without a frontend. Read the case study and see where it scales-> shorturl.at/6M8tC.

Agentic AI is already reducing logistics costs and bottlenecks. What teams are achieving: • 30% less equipment downtime • 40% higher throughput without added headcount • Automation across routing, warehousing, forecasting Get the guide -> shorturl.at/iNU6F

Running GPU workloads on GKE Autopilot without idle resources? Our engineers built a Flyte + GKE Autopilot setup with dynamic GPU scheduling: • No idle nodes • No manual tuning • Fully Autopilot-compatible Full guide here --> lnkd.in/eD9pHjCW

Poor API design leads to silos, delays, and broken visibility in terminal ops. We build and test high-performance APIs used by Reddit, Microsoft, and Fortune 500 teams, enabling real-time integrations across logistics systems. Let's build for reliability-> akvelon.com/contact-us/

Unloading a vessel is one of the most unpredictable port operations, and delays are costly. This Medium article shows how ML can forecast discharge time before unloading starts -> lnkd.in/eGnDFPF6 We achieved: • 65% variability explained • ~5 hrs more accurate scheduling

We gathered practical answers from our engineers in one place: Akvelon’s Top Engineering Articles of 2025 -> lnkd.in/d7zjbq4c. What you’ll find: • AI for brownfield systems • From pilots to production (up to 60% faster) • Real MLOps, agentic AI, and governance patterns

Satellite data is powerful — but how do you turn it into a usable system in one weekend? lnkd.in/d2x4ia7q – NASA Space Apps Seattle, our engineers built a prediction solution for farmers using NASA satellite data and won the Best Use of Data award.

Techarena 2026 | Stockholm | Feb 11–12 Kate Nyzhehorodtseva, Akvelon’s Director of New Business Development, will be there, discussing AI agents, automation, and modernizing legacy platforms. Reach out to her on LinkedIn to book a consultation: linkedin.com/in/kate-nyzheh…