Our AI engineering services
Production-ready AI pipelines
We build AI pipelines designed for scale, monitoring, and adaptation to real system constraints. Working PoCs require significant engineering to become production systems that handle actual operational demands reliably over time.
RAG architecture and context engineering
We create RAG systems that consistently retrieve the right information at the right time, ensuring AI responses remain accurate, contextually relevant and operationally reliable.
Evaluation and observability
We implement evaluation pipelines and observability frameworks that provide ongoing insight into accuracy, relevance, and reliability, allowing systematic optimisation throughout the system lifecycle.
ML at scale
Machine learning applications require infrastructure designed for sustained operation under real load. We build and deploy ML systems that perform consistently and predictably in demanding production environments.
From PoC to production delivery
We guide the complete engineering journey from prototype to operational system, covering architecture decisions, model selection, CI/CD integration, guardrail implementation, and go-live support.
Agentic workflow development
Multi-step AI systems that reason, plan, and execute complex tasks autonomously require careful framework design. We develop agentic workflows that enable sophisticated AI behaviour while maintaining appropriate human oversight and operational boundaries.
Let's discuss your AI engineering needs
Our approach to AI engineering
Moving from successful AI demos to production systems presents significant technical and operational challenges. We bridge this gap by combining engineering expertise with strategic insight, using evaluation and observability as the foundation of every delivery.
From experimentation to scaling
PoCs demonstrate that AI concepts work technically. Moving to production requires architectural decisions that can evolve with your organisation's needs. We design systems with scalability in mind from the beginning, so successful pilots can expand to broader deployment without requiring complete architectural reconstruction.
Evaluation as engineering standard
Without evaluation pipelines, you can't determine if AI systems perform better or worse after changes. Structured evaluation and observability layers provide continuous insight into accuracy, relevance, and behaviour. At Etex, Langfuse runs in production for observability and batch evaluations.
Production-first thinking
Real environments place different demands than laboratory conditions. From ambient noise in theme parks to compliance requirements in advisor workflows, design considerations address constraints that really matter, not ideal demo conditions.
Why work with us?
Production engineering experience, not lab results
Our AI engineers build and maintain systems running live in demanding environments, from busy theme parks to daily operations of national purchasing cooperatives. This hands-on experience informs every new project with practical insights about what works in production.
Evaluation and observability as engineering standard
Beyond building AI systems, we build the tools to monitor and improve them. Evaluation pipelines and real-time observability are integral to every delivery, not optional additions implemented later.
Complete engineering process guidance
From architecture and model selection through CI/CD integration, guardrail design, and production go-live: one partner handles the entire journey without handovers between strategy and implementation teams.
Engineering focused on measurable outcomes
AI engineering at iO targets two concrete results: better customer experiences through intelligent interfaces and conversational AI, and smarter operations through automation and process optimisation. Technology serves purpose, not the reverse.
