AI Solutions
AI features that work in production, not just in demos.
Most "AI features" fall into two categories: things that work in demos but embarrass you in production, and things that actually make the product better. The difference is usually in the details nobody talks about — how you handle bad inputs, what happens when the model is wrong, how you keep it fast and affordable at scale.
What this involves
Connecting models to your actual data
Retrieval-augmented generation done correctly means your answers are grounded in something real, not hallucinated. We build the retrieval layer, the chunking strategy, the reranking — all the parts that determine whether users actually trust the output or dismiss it after the second wrong answer.
Agents that don't go off the rails
LLM agents are powerful and surprisingly easy to get wrong. We build the guardrails, fallback logic, and observability that keep an agent useful rather than unpredictable. If it can affect real data or spend money, it needs to be conservative by default and auditable after the fact.
Fine-tuning when it's actually worth it
Fine-tuning is expensive and often unnecessary. We'll tell you when prompting and RAG are good enough — and when you genuinely need it. If you do need it, we handle data prep, training runs, evaluation, and deployment so the investment actually pays off.
Making sure it runs in production
A model that costs €0.50 per request and takes 8 seconds to respond isn't a product, it's a prototype. We work on latency, caching, model selection, and batching so your AI feature is usable at a price your margins can support.
This is a good fit if…
- You have a working product and want to add AI capabilities that aren't gimmicks
- You've tried building something AI-powered and it doesn't work reliably enough to ship
- You're spending too much on inference and need to bring costs under control
- You want someone to tell you honestly whether AI is actually the right approach for your problem
- Your AI feature works in the demo but fails when real users try edge cases
Technologies we use
We work in the stack you already have. Here's what we typically reach for in this area.
Common questions
Which AI providers do you work with?
OpenAI, Anthropic, Google, Mistral, and open-source models via Hugging Face or Ollama. Provider choice is usually a cost and compliance decision — we'll help you make it based on your actual requirements.
What's the difference between RAG and fine-tuning?
RAG retrieves your data at query time and grounds the model's response in it. Fine-tuning trains the model's weights on your data. For most use cases, RAG is cheaper, faster to iterate on, and more maintainable. We default to RAG and will tell you when fine-tuning is genuinely worth it.
How do you evaluate whether an AI feature is working?
We build evaluation pipelines — test sets of real questions with expected answers, automated scoring, and human review for edge cases. The goal is to know when the model is wrong before your users tell you.
Can you audit an AI system we already have?
Yes. We'll look at the prompt design, retrieval quality, latency, cost structure, and failure modes — and give you an honest assessment of what's worth fixing and in what order.