AI systems aren’t fully predictable, so understanding them is key. Our setups make data flows, model responses, and assumptions transparent — documented, adjustable, and built around your needs.

Big foundation models are impressive but often unnecessary. We prefer smaller, fine-tuned LLMs that integrate seamlessly into existing systems. Combined with smart prompts and retrieval-augmented context, they’re fast, cost-effective, and GDPR-compliant.

Every decision is traceable: from which model we use to how architecture choices are made. We build systems you can understand, not just trust.

Sensitive data doesn’t belong in random clouds. For critical applications, we rely on self-hosted or EU-based models, keeping personal data and IP under your control.

It depends on the phase. And honestly, it’s still early to put exact numbers on it. What’s clear so far: AI speeds up concept work, prototyping, and even parts of implementation. Since we bill on a time-and-material basis, less time usually means lower costs.

• Early phases: This is where AI makes the biggest difference. Concepts, user flows, and design prototypes come together much faster, sometimes within just a few days.
• Implementation: Noticeable efficiency gains in repetitive work like code generation, testing, and refactoring.
• Mature or complex systems: The effect tapers off; integration, QA, compliance, and migration effort start to dominate.

Our take: AI tends to lower costs most effectively in the early stages. In long-running or complex projects, it still helps, just with a smaller impact.

That really depends on what you’re building. Smaller projects can often benefit from open-source models that run locally. More advanced use cases involving large language models may add license or usage fees. Either way, we’ll work with you to design a transparent setup that fits your budget and makes sense for your goals.

There are three key areas every team should keep in mind when AI enhances software development:

• Resource consumption: Large models can be energy-intensive and increase operational costs.
• Data privacy: Cloud-based models may expose sensitive data to external systems.
• Bias & transparency: AI reflects bias from its training data, and its reasoning isn’t always explainable.

How to manage these risks responsibly By integrating AI thoughtfully into your development workflow:

• Always review AI output critically and treat it as a starting point, not the final result.
• Code reviews and tests remain essential to maintain quality and security.
• Wherever possible, use smaller, locally hosted models to stay independent, efficient, and sustainable.

The concern that AI might replace human expertise is understandable. And honestly: what profession isn’t asking that question right now? In software development especially, we can clearly feel how powerful these tools are. But in practice, one thing stands out: AI enhances software development best when it acts as an assistant, not a replacement.

It takes over repetitive tasks and frees up time for higher-level work. Architecture, API design, strategic decisions. What really matters is staying in touch with the craft:

• Work without AI from time to time to strengthen problem-solving and creativity.
• Keep documentation and transparency clear, for example by marking commits when AI-generated code is used.
• Use AI as a learning partner: it helps developers explore new patterns instead of losing knowledge.

Good developers don’t lose their skills through AI, they expand them. When used thoughtfully, AI enhances software development by making teams more productive, creative, and technically curious. It’s not a threat to craftsmanship, but a tool that amplifies it.

It depends! But here’s what a typical setup might be at Peerigon: A smart search powered by Mistral for embeddings (fast and cost-efficient), Claude for reasoning and complex queries, and a local Llama model for client data, all connected through LiteLLM, one API call, three models, seamless integration.

That’s how AI enhances software development in real projects by combining specialized systems intelligently.

Dependencies and quality problems usually arise when AI is used everywhere without clear purpose. The solution: a thoughtful integration strategy.

• Use AI for well-defined tasks like prototyping, testing, or standard code generation.
• Integrate AI output into existing quality processes like reviews, tests, architectural decisions.
• Establish clear governance and usage guidelines within your team.
• Select models consciously: sometimes a smaller, local LLM beats a large cloud-based one.

This way, AI enhances software development without creating new dependencies by speeding up processes while keeping control and quality in your hands.

The best way in is to start small and focused, for example with test automation, documentation preparation, or an early prototype. We help you quickly develop an MVP, gather first-hand experience, and measure real value. From there, you can scale step by step and let AI enhance your software development at your own pace.