Recent developments in retrieval-augmented generation highlight a critical architectural flaw: RAG systems are fundamentally unsuited for numerical aggregation, and expanding context windows exacerbates rather than resolves the issue. Engineers at EmiTechLogic and independent developers demonstrated that when RAG processes structured datasets, it performs pattern matching rather than computation, producing highly confident but mathematically inaccurate outputs. This phenomenon, termed Error Observability Collapse, describes a state where larger context windows increase response length and perceived authority while drastically reducing error detectability. Standard RAG pipelines flatten tabular data into serialized text, retrieve top results via keyword similarity, and instruct large language models to sum, group, or average the values. Because models lack deterministic computational logic, they approximate totals based on partial data slices. A single dataset containing 100,000 rows showed that even an 8,000-row context window, representing less than 1 percent of the data, produced aggregated answers with over 50 percent error margins. The output remains professionally formatted, rendering statistical inaccuracies nearly invisible to human reviewers without external verification. To address this, developers have deployed a dual-path architecture that eliminates retrieval from analytical queries. A lightweight Query Router classifies incoming natural language prompts using tiered intent detection: aggregation verbs and numeric comparisons trigger deterministic computation, while retrieval signals direct queries to standard RAG pipelines. The computation path routes requests to a Semantic Engine that executes a single deterministic pass over the complete dataset. This approach bypasses embeddings, vector search, and probabilistic inference entirely. Benchmarking across seven standard analytical operations confirmed the system reliability. The semantic engine processed the full 100,000-row dataset in under 200 milliseconds, delivering exact results with zero external dependencies. The query router itself executes classification in microseconds. Combined routing and execution latency averaged 250 milliseconds, outperforming embedding generation and vector search overheads. A comprehensive test suite of 159 unit tests validated parsing accuracy, edge case handling, and numerical precision across multiple data formats. The architectural shift reframes RAG as a specialized tool for semantic lookup rather than a general-purpose data processor. By enforcing a strict separation between probabilistic retrieval and deterministic computation, organizations can eliminate silent calculation errors in business intelligence and operational dashboards. The complete implementation is publicly available as an open-source repository. The framework currently supports single CSV files and regex-based intent classification, with database integration and LLM-assisted routing slated for future iterations. This development underscores a broader industry realization: scaling context windows does not compensate for fundamental algorithmic mismatches between generative models and structured data computation.