Our RAG and context engineering services
RAG architecture and implementation
From document ingestion and chunking strategy to retrieval pipeline and model integration. We design RAG architectures that work in production, not just in a demo environment with hand-selected test documents.
Hybrid retrieval
Pure semantic search misses exact terms. Pure lexical search misses context. We combine both, supplemented with Reciprocal Rank Fusion re-ranking, for retrieval that performs well on conceptual questions, specific product names and technical codes.
Knowledge infrastructure and data-as-a-product
AI agents are only as good as the data they consult. We design knowledge infrastructures where domain teams manage and enrich their own data; not as a technical exercise, but as part of their normal working process. This keeps your AI current without creating central data overhead.
Document pipelines and source integration
Knowledge is spread across websites, PDF manuals, CMS systems, SAP and Salesforce. We build automated pipelines that merge those sources, normalise them and make them available to your AI agent, including version control and source attribution.
Context engineering and prompt architecture
The quality of a RAG response depends on more than retrieval quality. We design the complete context stack: what information the model receives, in what order, with what instructions, and how you systematically evaluate and improve those decisions.
RAG evaluation and quality improvement
How well does your AI platform answer the questions that come in? We build automated evaluation pipelines based on relevance, accuracy and source attribution, so you can measure and steer quality improvement.
Let's discuss your AI engineering needs
Our approach to RAG and context engineering
RAG is a knowledge architecture discipline. The quality of your retrieval layer depends directly on the knowledge infrastructure it sits on: how documents are ingested, structured and kept current. Get that right, and your AI agents give reliable, traceable answers to the questions that come in. Our approach covers the complete knowledge layer, from source integration and data quality to evaluation and continuous improvement.
Context engineering starts at ingestion
Most RAG problems are ingestion problems. How you split documents, how you handle tables and structured data, how you deal with version conflicts between sources. These decisions determine retrieval quality before a single user query is run. We design the ingestion strategy as an architecture decision, not as a configuration choice.
Knowledge infrastructure as domain ownership
When domain experts can manage the knowledge sources they understand best, data quality improves structurally. We design knowledge infrastructures as data-as-a-product: domain teams manage and enrich their own data via structured interfaces, without depending on a central data engineer for every update.
One knowledge layer, multiple access paths
The same knowledge infrastructure should be accessible to a customer-facing chatbot, an internal advisor tool and an agentic workflow, without rebuilding the retrieval logic for each. We design knowledge delivery as a composable layer: accessible via API, MCP or CLI, depending on what each consuming system requires.
Why work with iO?
Production RAG, not demo RAG
Building a RAG system that works in a demo is straightforward. Making it work on a real-world document collection is a different challenge. At SBB, we indexed 1,000+ documents across three sector-specific assistants. At Etex, we combined semantic search, BM25 and RRF in a live customer service platform.
Knowledge architecture and AI architecture
Where most AI vendors think in models, we think in data. RAG is first and foremost a data architecture question: how do you structure sources, who manages them, how do you keep them current? We bring the integration depth needed to connect SAP, Salesforce, ContentHub and your own CMS to the knowledge layer of your AI agent.
AIECC knowledge across projects
Patterns we discovered at SBB for sector-specific knowledge partitioning, we adapt at Etex for multi-brand product data. Our AI Engineering Competence Centre ensures that architecture lessons are shared across projects, so your project benefits from what we've learned before.
AIECC knowledge across projects
Patterns we discovered at SBB for sector-specific knowledge partitioning, we adapt at Etex for multi-brand product data. Our AI Engineering Competence Centre ensures that architecture lessons are shared across projects, so your project benefits from what we've learned before.
