RAG · Enterprise SaaS · Knowledge Copilot
RAG Rebuild · LLMOps · Enterprise SaaS
Enterprise Knowledge Copilot
— RAG Rebuild
Rescued a hallucinating RAG implementation built by a previous vendor. Rebuilt from scratch with hybrid retrieval, reranking, a RAGAS-based eval harness, guardrails, and full LangSmith observability. Retrieval faithfulness went from unreliable to measurably accurate — solid in production for 6+ months.
Results
6 mo+
Stable in production — no regressions
RAGAS
Evals passing — faithfulness & relevance measured on every deploy
~0
Hallucinations in production after rebuild
100%
Trace coverage — every query logged and observable
The Problem
The client — a FinTech SaaS company — had contracted a vendor to build an internal knowledge copilot for their engineering and ops teams. The system was supposed to answer questions about internal policies, product documentation, and runbooks.
It hallucinated constantly. Engineers stopped trusting it within weeks of launch. There was no evaluation framework, no observability, and no way to tell whether any given answer was grounded in the actual documents or invented. They came to us to rebuild it properly.
❌ Before — what we inherited
- Naive chunking — fixed 512-token splits, no semantic awareness
- No reranking — top-k retrieval with no relevance scoring
- No eval framework — impossible to measure quality
- No observability — queries vanished into a black box
- No guardrails — model free to hallucinate confidently
- No prompt versioning — changes untraceable
✅ After — what we shipped
- Hybrid retrieval — semantic + BM25 with metadata filtering
- Cohere Rerank — relevance-scored before generation
- RAGAS eval harness — faithfulness, relevance, precision measured
- LangSmith — 100% trace coverage, cost and latency monitored
- Guardrails AI — output validation and hallucination detection
- Prompt versioning — every change tracked and deployable
RAG Maturity Ladder — Where We Took Them
We don't just rebuild RAG — we move clients up the maturity ladder to the level their use case actually needs. For this enterprise knowledge copilot, that meant Advanced RAG with a full eval and observability layer.
Level 1
Naive RAG
Fixed chunking, top-k cosine similarity, no reranking — what the previous vendor built.
Level 2
Advanced RAG
Hybrid retrieval, semantic chunking, reranking, query decomposition, eval harness. What we rebuilt.
✓ Deployed
Level 3
GraphRAG
Knowledge graph over documents — ideal for complex multi-hop reasoning across entities.
Level 4
Agentic RAG
Agent decides which retrieval strategy to use per query — adaptive, multi-source, multi-step.
Architecture
Rebuilt RAG pipeline — query to grounded answer
❓
User Query
Natural language
→
🔍
Hybrid Retrieval
Semantic + BM25
→
→
🛡️
Guardrails
Validate context
→
🤖
LLM Generation
Grounded answer
→
System components
1
Retrieval Layer
QdrantBM25Hybrid searchMetadata filteringSemantic chunking
2
Reranking
Cohere RerankCross-encoder scoringTop-k selection
3
Generation
LangGraphOpenAI GPT-4Structured promptsPrompt versioning
4
Guardrails
Guardrails AIOutput validationHallucination detectionPII check
5
Observability & Evals
LangSmithRAGASDeepEval100% trace coverageCost monitoring
6
Ingestion Pipeline
LlamaParseSemantic chunkingMetadata enrichmentAuto-sync
LangGraphOpenAI GPT-4QdrantCohere Rerank
LangSmithRAGASDeepEvalGuardrails AI
LlamaParseFastAPIPostgreSQLAWS
"After struggling with a hallucinating RAG implementation from another vendor, we brought Analytico in to rebuild our AI data architecture from scratch. They designed a proper hybrid retrieval pipeline with reranking and evals — and explained every architectural decision clearly. The system has been solid in production for six months."
— VP Engineering, FinTech SaaS