Grounded AI Knowledge
Assistant

Domain-specific retrieval-augmented assistant answering technical inquiries with strict ground-truth citation, confidence scoring, and zero hallucination risk.

Domain Assistant RAG Pipeline Citation Engine Confidence Scoring Hallucination Defense
CASE STUDY SPOTLIGHT
Instant retrieval.
Verifiable citations.
ACCURACY
100% cited source facts
CONFIDENCE
Deterministic thresholds
SPEED
Sub-second synthesis
EXECUTIVE SUMMARY

Operational Context & Objectives

An engineering organization maintained over 30 years of mission-critical design documentation, operating manuals, maintenance bulletins, and compliance certificates across heterogeneous formats (PDFs, CAD drawings, scanned legacy documents, and intranet wikis).

Senior engineers spent an average of 6.5 hours per week searching for authoritative technical standards and prior test results. Previous attempts to use off-the-shelf generative AI tools had failed catastrophically due to hallucinations, missing citations, and confidentiality concerns regarding proprietary engineering IP.

THE SOLUTION

Engineering Architecture & Delivery

I designed and implemented an enterprise-grade Grounded AI Knowledge Assistant built on a strict Retrieval-Augmented Generation (RAG) framework. Rather than allowing the language model to answer from parametric memory, the system enforces a deterministic retrieval pipeline that pulls authoritative excerpts before synthesizing responses.

A hybrid retrieval engine combines semantic dense vector embeddings with lexical BM25 keyword matching to accurately surface obscure part numbers, acronyms, and technical phrases. Every generated answer is programmatically linked to exact document references, page numbers, and version tags, allowing engineers to verify ground truth in seconds.

Hybrid Semantic & Lexical Retrieval Combines dense vector search with sparse keyword indexing, ensuring precise matches for specialized part numbers, model codes, and acronyms.
Deterministic Citation Enforcement The model is bounded to respond strictly from retrieved contexts. If the context does not contain verified proof, it explicitly declares lack of data.
Secure IP Boundary & Zero Training Architected with zero-data-retention API agreements and strict role-based document access, ensuring confidential design IP never leaks.
Automated Evaluation Benchmarking Continuous automated regression tests evaluate factual recall, precision, and citation integrity across 200+ canonical technical queries.
Multi-Format Ingestion Pipeline Automated parsing and chunking pipeline capable of ingesting complex PDF layouts, tables, schematics, and legacy scans with OCR.
RELATED CAPABILITY

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This project leverages core methodologies from the AI & Intelligent Applications consulting practice.

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