
PLANEKS delivers AI-augmented development services designed to improve delivery speed, code quality, and long-term maintainability by combining engineering expertise with targeted AI automation. We bridge the gap between what AI tools are marketed to do and what they actually deliver in real development teams.
In practice, AI is effective for pattern-based work such as boilerplate generation, test scaffolding, and refactoring, but is limited when contextual judgment is required, such as with ambiguous requirements, legacy complexity, and architecture decisions tied to product history.
In our model, AI handles repetitive implementation, while engineers own the reasoning layer. This enables faster delivery, higher consistency, and lower long-term cost.
Benefits
Advantages of AI-augmented Development
Most project delays come from process friction, such as context switching, ramp-up time, and repetitive overhead. AI-augmented development services, when applied at the right layers, reduce bottlenecks without changing the engineering fundamentals.
Faster iteration cycles in backend-heavy systems
More efficient use of developer time
Consistent code quality across teams
Scalable output without proportional team growth
How we do it
How We Integrate AI Into Real Development Workflows
At PLANEKS, we integrate AI tools directly into the engineering workflow at the layers where they produce consistent, controlled, beneficial, and secure outcomes.
AI-assisted coding pipelines
AI-driven test generation and validation
All of this is integrated into our CI/CD pipeline. AI performs as a core instrument in the workflow, adopted at every stage where it ensures optimization and enhanced efficiency.
Architecture decisions
Where AI Fits in the Stack
Our team applies AI-driven intelligence to your workflows with deliberate architectural placement, ensuring it supports the system while improving performance and development speed.
AI at the development layer vs the product layer
When to use LLM APIs vs fine-tuned models
Handling latency, cost, and reliability in AI-assisted systems
Case Study: AI-Assisted Refactoring of a Legacy Python System
Refactoring existing systems is often harder than building new ones because there is no clean slate, only accumulated technical decisions, workarounds, and debt that must be explored before proceeding with any changes. One of the longest-running projects of our AI-augmented software development company is EuropaFoodXB, a UK-based European food retailer with over 7,000 products, an active eCommerce store, and a custom CRM system for order management. The platform was built on Django and Django Oscar, which are solid foundations. However, over time, it accumulated complexity, making changes slower and riskier than required.
The problem primarily lied in the interconnected nature of the system: a parallel migration from an old database to a new one with entirely different schemas, a custom multi-region delivery engine coordinating four courier services with overlapping rules, integrations with DPD for automated tracking number generation, and daily operational workflows such as order reporting and abandoned cart recovery, all developed iteratively without a unified architectural structure.
To address this, our team applied AI-assisted code analysis and refactoring to systematically review the codebase, surface refactoring opportunities, and generate restructured implementations for senior engineer review. Every AI-generated change was manually validated before reaching production, ensuring correctness while accelerating iteration. This approach reduced technical debt and improved maintainability across the system. As a result, AI augmented feature development became significantly faster, improving development speed by 35-50%, while simultaneously reducing early-stage bugs and improving onboarding time for new developers. The system became more stable, easier to maintain, and faster to evolve for the client’s team.
Advantages
Use Cases Where AI Augmentation Delivers Measurable ROI
AI augmentation provides clear ROI in areas where development work is structured, repeatable, and engineering-heavy.
API development and integration-heavy platforms
Identify the highest-impact opportunities to accelerate development and reduce costs.
Case studies
Proud projects make us excel
Already have a plan? Or are you just getting your head around it?
Our approach
Engineering-First, Not Tool-First
We focus on where AI can meaningfully support delivery without compromising system understanding, code quality, or long-term maintainability.
3 Principles
Testimonials
What do people praise about PLANEKS?
Cooperation
Engagement Models for AI-Augmented Development
We are flexible in our engagement models and adapt our approach to each specific case. Our core cooperation models for AI-augmented software engineering are as follows.
5.0/5.0
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