Governed RAG & Institutional Memory
Organizations waste valuable hours trying to locate operational specifications, checklists, or historical research across disjointed directories. We design Retrieval-Augmented Generation (RAG) systems that extract, parse, and index unstructured manuals and telemetry files, offering natural language access with strict page-level source references.
We deploy advanced retrievers utilizing models like Cohere Command R+ for multi-lingual document grounding, Claude 3.5 Sonnet for complex cross-table reasoning, and local open-weight embeddings like Qwen-2.5-Coder to keep search parameters inside private servers.
RAG Vector & Pipeline Configuration Specification
We build our retrieval systems using optimized data layers and pipeline parameters depending on corporate data sizes:
| Data Category | Chunking Strategy | Vector DB Placement | Primary Ingestion Model | Verification Check |
|---|---|---|---|---|
| Engineering SOPs / Manuals | Fixed 512-Token Overlap | pgvector (Postgres) | Claude 3.5 Sonnet / Llama 3.1 | Strict page & paragraph verification |
| Telemetry & Well Data | Tabular row-by-row extraction | ChromaDB (Local Cache) | Gemini 1.5 Pro (Big Context) | SQL cell cross-validation |
| Financial Indices / Reports | Semantic section boundaries | pgvector (Postgres) | Cohere Command R+ | Ticker index cross-matching |
| Standard Corporate Wikis | Page-level Markdown sync | Isolated local index (Obsidian) | Qwen-2.5-Coder / Llama 3.2 | Git commit hash verification |
Detailed Ingestion Pipeline Blueprint
Our 3-stage RAG ingestion pipeline guarantees data security and high citation precision:
1. Document Processing
Automated parsing pipelines utilizing OCR to extract tables, diagrams, and structural text from raw PDF files, preserving document margins and headers.
2. Embeddings & Search
Converting chunks into dense vectors using local embedding models, stored inside pgvector schemas with departmental access tags.
3. Grounded Synthesis
Our prompt compilers force the model to answer queries using retrieved chunks ONLY, listing verified source links to prevent hallucination errors.