AI engineering: stable, scalable and production-ready

Production-ready AI pipelines
A working PoC is not production. We build AI pipelines that scale, are monitored and adapt to the reality of your systems, from first iteration to live environment.
RAG architecture and context engineering
Retrieval-augmented generation that retrieves the right information, at the right time, in the right form. We design retrieval strategies, chunking logic and hybrid search layers that make your AI reliable.
Evaluation and observability
You can't improve AI you can't measure. We build evaluation pipelines and observability frameworks so you continuously track, and adjust, the accuracy, relevance and reliability of your AI.
ML at scale
From recommendation models to computer vision and forecasting models, we build and operate machine learning applications that perform under real load, in real environments.
From PoC to production delivery
We guide the complete process: architecture decisions, model selection, CI/CD integration, guardrails and go-live. So your AI investment doesn't stall in the experimentation phase.

Our approach to AI Engineering

The gap between a convincing AI demo and a system that holds up in production is where most AI investments disappear. Demo conditions are clean data, controlled inputs, patient reviewers. Production conditions are messy data, edge cases, time pressure, zero tolerance for inconsistent output. We bridge that gap by bringing engineering and strategy together, with evaluation and observability as the foundation of every delivery.

From experimentation to scaling
A PoC proves technical feasibility. Production requires architectural choices that grow with your organisation. We design for scalability from the start, so a successful pilot doesn't need to be rebuilt.
Evaluation as engineering standard
Without evaluation pipelines, you don't know if your AI system is performing better or worse after each change. We build structured evaluation and observability layers so you always have insight into accuracy, relevance and behaviour. At Etex, Langfuse runs in production for observability and batch evaluations, that's engineering discipline, not just building.
Production-first thinking
Real environments place different demands than a lab. From ambient noise in a theme park to compliance requirements in an advisor workflow, we design for the constraints that actually matter, not for ideal demo conditions.

Why AI Engineering with iO?

Production experience, not lab results
Our AI engineers build and maintain AI systems that run live in demanding environments, from a busy theme park to the daily operations of a national purchasing cooperative. We bring that hands-on experience to every new project.

Evaluation and observability as standard
We don't just build the AI system, we also build the tools to monitor and improve it. Evaluation pipelines and real-time observability are part of every delivery with us, not a nice-to-have afterwards.

From architecture to go-live
We guide the complete process: from architecture and model selection through CI/CD integration, guardrail design and production go-live. One partner, no handovers.

AI solutions for customer experience and operations
AI Engineering at iO focuses on two concrete outcomes: better customer experiences through intelligent interfaces and conversational AI, and smarter operations through automation and process optimisation. No technology for technology's sake.

Frameworks, tools and platforms

We work technology-agnostically: the choice of frameworks and platforms depends on your context, your systems and your organisation's requirements, not our partnership agreements.