Advanced context pipeline with RAG ingestion, token compression, relevance ranking, and intelligent prompt assembly — achieving 68% token savings while maintaining 97% information recall across 200K context windows.
Cascade AI, a Series B startup based in Seattle, WA, had built six AI-powered features across their product — but every single one was hitting the same wall. Their RAG pipeline had a retrieval accuracy of just 45%, meaning more than half the context fed to the model was irrelevant or wrong. Prompts were bloated with redundant information, burning through $47K/month in API costs. Worst of all, their hallucination rate had climbed to 23% — and three enterprise customers had already flagged it as a deal-breaker for renewal.
The root cause wasn't the LLM — it was everything happening before the LLM. Their documents were chunked with fixed 512-token windows regardless of content type (code docs were being split mid-function, tables were being fragmented into meaningless rows). Retrieval used basic cosine similarity with no reranking, so the top-k results were often semantically similar but factually irrelevant. And prompt assembly was a string concatenation script that a junior engineer had written in a weekend — no structure, no priority, no quality gates.
We rebuilt Cascade's entire context pipeline from ingestion to injection. The new system uses content-aware chunking that understands document structure — code is chunked by function/class boundaries, tables are kept intact with headers, and documentation is split at semantic section breaks. We implemented a hybrid retrieval stack combining dense embeddings (Cohere), sparse BM25, and a cross-encoder reranker that tripled retrieval accuracy from 45% to 97%. A semantic compression layer strips redundant information and condenses context by 68% while preserving all critical relationships and facts.
The pipeline includes quality gates at every stage — if retrieval confidence drops below threshold, the system automatically broadens the search or flags for review rather than injecting garbage context. We built a real-time monitoring dashboard that tracks token efficiency, retrieval accuracy, hallucination rates, and latency per feature, giving Cascade's AI team full visibility into context quality. The entire pipeline runs in 340ms end-to-end, down from 2.8 seconds.
Multi-format document processing with chunking strategies optimized per content type — code, docs, tables, and conversations.
Semantic compression that reduces token usage by 68% while preserving critical information and relationships.
Hybrid search combining dense embeddings, sparse BM25, and cross-encoder reranking for 97% retrieval accuracy.
Dynamic prompt construction that organizes context by relevance, recency, and semantic structure.
Intelligent caching of frequently accessed context with TTL policies and cache invalidation on source updates.
Real-time tracking of retrieval accuracy, token efficiency, hallucination rates, and user satisfaction.
Built with cutting-edge AI infrastructure for maximum reliability and performance.
Our AI features went from embarrassing to enterprise-grade overnight. The hallucination rate dropped from 23% to 2%, and three enterprise customers who were threatening to churn renewed their annual contracts within a week of seeing the improvement. The Rivan.ai team didn't just fix our RAG pipeline — they taught our AI team how to think about context engineering as a discipline. The monitoring dashboard alone was worth the investment.
Let's build a context pipeline that makes your AI actually reliable.
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