AI Architecture
LLM integration, retrieval-augmented generation, and enterprise AI governance for organisations moving beyond pilots.
Enterprise AI adoption fails at the architecture layer. Organisations that get this right — data pipelines, context design, governance, security boundaries — move from promising pilots to production systems that can be trusted and scaled. Those that don't accumulate technical debt and compliance risk.
NILUS brings enterprise architecture rigour to AI adoption — treating LLM integration not as a standalone technical project but as a capability embedded in data, security, and operational architecture.
What We Deliver
LLM Integration Architecture
Designing how large language models connect to enterprise systems — API orchestration, context management, prompt engineering patterns, and fallback strategies.
RAG System Design
Retrieval-augmented generation architecture — vector database selection, chunking strategy, embedding pipelines, hybrid search, and relevance tuning for enterprise knowledge bases.
AI Governance Frameworks
Defining AI operating models — model lifecycle management, human-in-the-loop checkpoints, bias monitoring, audit trails, and alignment with EU AI Act requirements.
Secure Enterprise AI
Data residency, PII handling, prompt injection mitigation, model access controls, and integration patterns that keep sensitive data inside enterprise boundaries.
AI Platform Architecture
MLOps pipeline design, model serving infrastructure, feature store integration, observability for AI systems, and multi-model orchestration patterns.
AI Adoption Roadmaps
Pragmatic sequencing of AI use cases across the enterprise — identifying high-value opportunities, capability prerequisites, and governance maturity requirements.
Architecture Challenges We Address
- LLM pilots that work in demo but fail in production due to context window and latency constraints
- RAG systems with poor retrieval quality because chunking and embedding strategy wasn't designed carefully
- Sensitive data leaking into external LLM providers without adequate guardrails
- No governance model for how AI outputs are reviewed, audited, or overridden
- AI projects blocked by legal and compliance teams without a clear framework for approval
- AI capabilities that can't be explained to regulators or auditors
- Model drift and performance degradation with no monitoring in place
Technologies & Platforms
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Moving AI from Pilot to Production?
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