Production-Grade Enterprise RAG with Guardrails, Evaluation & AI Ops
Challenges
The enterprise’s RAG-based assistant stalled at proof-of-concept due to low trust in its outputs. High hallucination rates, inconsistent retrieval, missing governance and lack of evaluation. As a result, adoption was blocked, engineering teams resorted to firefighting and the solution remained limited to demos rather than enterprise use.
Outcome
The production-grade RAG implementation reduced hallucinations by 70–80%. Predictable behaviour and evaluation-gated releases increased platform reliability.
Solution
Enterprise Knowledge Assistants (RAG)
Challenges
Solution
Technology Stack
Outcomes
Summary: Production-Grade Enterprise RAG
For one of the top UK-based enterprises, a flagship RAG-based knowledge assistant was introduced with the intent of accelerating access to internal knowledge through AI. Early demonstrations showed promise, but the system began to struggle as expectations shifted from experimentation to real usage.
The enterprise needed a system that responds well to multiple users and document types. The absence of a solid platform made issues around answer accuracy, retrieval consistency, and operational ownership increasingly visible. Legal, risk and operations teams raised concerns, limiting the platform’s use to controlled scenarios rather than broader adoption.
Slowly, the organisation was forced to step back from surface-level fixes and initiate a deeper examination of the platform’s architecture, governance and operational foundations.
The engagement marked a turning point, as the platform issues were addressed through a structured, engineering-led approach.
Client Problem with Enterprise RAG Architecture
The enterprise had invested heavily in AI experimentation, but progress had stalled at the proof-of-concept stage. A high-visibility RAG-based knowledge assistant had attracted executive attention, yet confidence in its outputs remained low. Various teams were unwilling to adopt and plans to extend the assistant across additional business domains came to a standstill. It was clear that the challenge was not the intent, but trust. Lack of confidence in accuracy and ownership, the platform could not move beyond demonstrations.
Technical pain points:
The system showed clear signs of instability and immaturity under even moderate usage:
Hallucination rates were consistently too high for enterprise use
Retrieval quality varied significantly across document types, including PDFs, policy documents and technical specifications
No formal evaluation framework or quality baseline existed
Prompt-level tweaks were being used to mask deeper retrieval failures
Document-level access control and governance were absent
Latency was unpredictable when multiple users accessed the system
There was no monitoring, drift detection or operational runbook to support production use
Operational impact of the technical pain points:
AI-generated outputs could not be trusted for decision-making
Legal and compliance teams formally blocked wider rollout
Engineering effort was consumed by prompt firefighting rather than platform improvement
The system remained suitable only for demos, not enterprise- level deployment
Root Cause Analysis
Instead of jumping to a conclusion, Cloudaoen AI engineers started with a root cause analysis. With some deep evaluation, it was clear that the problems were actually with the architectural and operational gaps. The system had been built as a demo rather than a production platform, which resulted in:
Retrieval architecture flaws: A vector-only retrieval approach, without hybrid scoring or reranking, introduced excessive noise and inconsistent recall.
Chunking without metadata: Enterprise documents were split naïvely, breaking semantic continuity and losing context.
No evaluation layer: Accuracy, relevance and hallucination were discussed anecdotally rather than measured systematically.
Zero governance: There was no access control, lineage tracking or ownership model at the document level.
No AI Ops capability: The platform lacked mechanisms for drift detection, regression analysis or SLAs.
PoC mindset: Failure modes, controls and recovery paths were never designed in.
The result was silent degradation, unpredictable responses and a complete erosion of enterprise trust.
Solution Architecture: Enterprise RAG Architecture
Cloudaeon AI experts took a unique approach; rather than attempting incremental fixes, the system was re-architected as a production-grade AI platform, with discipline around governance, evaluation and operability.
As architectural gaps were one of the major issues, a target architecture was introduced:
Ingestion & Preparation:
Metadata-aware document ingestion capturing source, domain, sensitivity and ownership
Structured chunking strategies tailored for long-form and mixed-format enterprise documents
Embedding & Indexing:
Domain-appropriate embedding models
Controlled index refresh pipelines with built-in validation
Retrieval Layer:
Hybrid retrieval combining keyword and vector search
Cross-encoder reranking to improve relevance precision
Generation Layer:
Prompt templates constrained strictly by retrieved evidence
Explicit handling of context limits and fallback scenarios
Evaluation Layer:
LLM-as-a-judge pipelines for systematic quality assessment
Golden datasets to support regression testing
Automated scoring for accuracy, faithfulness, and hallucination
Guardrails & Governance:
Content filters and policy enforcement
Document-level access control
Full audit logs for queries and responses
Ops Layer:
Monitoring across latency, accuracy, and cost
Drift detection with alerting
Controlled deployment, rollback, and release gating
How We Delivered: Production-Grade Enterprise RAG with Guardrails, Evaluation & AI Ops
Delivery followed a step-by-step engineering-first approach, with quality gates applied before optimisation or expansion.
Rebuilt ingestion pipelines using metadata-first document modelling
Implemented structured chunking strategies aligned to document types
Introduced hybrid retrieval and reranking to stabilise relevance
Designed and deployed an automated evaluation framework using LLM-as-judge
Defined explicit quality metrics and baselines before tuning
Implemented guardrails covering policy enforcement, toxicity, and factual grounding
Introduced caching and concurrency controls to stabilise latency
Integrated monitoring dashboards for accuracy, latency, and cost
Established regression testing for every index or prompt change
All changes were validated against measurable quality metrics before promotion.
Technology Stack
Azure OpenAI / OpenAI GPT
Fabric Lakehouse for governed data foundations
Enterprise-grade vector databases
Hybrid retrieval engines
Cross-encoder rerankers
LLM-as-judge evaluation pipelines
API orchestration services
Monitoring and logging frameworks
Outcomes after Implementing Production-Grade Enterprise RAG
Hallucination rate: Reduced by approximately 70–80% from baseline
Answer relevance: Demonstrated measurable improvement across evaluation runs
Latency: Reduced by approximately 40% under concurrent load
Reliability: Predictable behaviour with defined failure handling
Release confidence: Platform changes are promoted only after passing evaluation gates
These outcomes provided the evidence required for enterprise-wide rollout approval.