Modular AI systems for real environments.
/01 Systems
The model is rarely the problem. Grounding is. Without structured retrieval, outputs drift. Context windows fill with noise. Answers hallucinate.
We build retrieval-first systems. Hybrid search with vector and keyword fusion. Scope-aware queries. Reranking before generation. Architecture designed for accuracy.
Security is structural: JWT authentication, encrypted OAuth tokens, tenant isolation, and scoped permissions. All of it built into the system boundary from the start.
/02 Capability
Design retrieval as a primary system layer, not a downstream add-on.
Vector + keyword strategies are structured before model invocation to ensure grounded outputs.
Data sources are abstracted as interchangeable connectors.
New integrations can be introduced without restructuring the core system.
Permissions, scopes, and identity layers are isolated and enforced at the system boundary.
Security constraints are treated as architectural inputs, not compliance afterthoughts.
Systems are containerised, versioned, and deployed with environment separation.
Infrastructure decisions account for latency, monitoring, and failure recovery.
/03 System 01
Retrieval Infrastructure
Modular retrieval system with multi-source ingestion, hybrid search, and production-grade infrastructure. Combines vector and keyword retrieval with RRF fusion and cross-encoder reranking. Supports Google Drive, local files, and browser uploads with idempotent sync. Includes OCR for scanned documents, scope-aware queries, and conversational context handling. Multi-tenant architecture with JWT authentication and containerised deployment. Currently undergoing external review prior to public release.
Private system walkthrough available upon request.
Request a private walkthrough →Architecture Diagram
/04 Constraints
Design for constraints early. Or fix them later at scale.
/05 Thinking
When teams build retrieval-augmented generation systems, they typically start with the model. They pick GPT-4 or Claude, wire up a basic vector database, and call it a day. Six months later, they're debugging hallucinations. The problem is rarely the model. It's almost always the retrieval.
Read the full article →Vector embeddings transformed document retrieval. Instead of matching keywords, we could match meaning. A query about "company earnings" could find documents discussing "financial performance" without those exact words appearing. Then teams deployed it to production and discovered the edge cases.
Read the full article →Retrieval gets you candidates. Reranking gets you the right candidates. The distinction matters more than most teams realize when building production RAG systems. The standard pipeline hopes the LLM figures out which chunks matter. This works until it doesn't.
Read the full article →Users don't trust AI features because they're powerful. They trust them because they're predictable. A feature that works brilliantly 80% of the time and fails catastrophically 20% of the time is worse than a feature that works adequately 100% of the time.
Read the full article →/06 About
Riot Haus is an independent AI systems studio specialising in retrieval-first architecture. We think most AI projects fail not because of the model, but because the hard stuff gets bolted on later: search, security, failure handling. If you're tired of demos that don't scale, we'd like to hear what you're building.