ARCHAEOLOGY EXPERT SYSTEM

(1) Overview of the Research Problem
Despite rapid advances in Large Language Models (LLMs), their deployment in professional knowledge-intensive sectors remains constrained by hallucinations and data gravity. LLMs frequently generate plausible but unsupported outputs when applied to specialized corpora, while organizations hold vast quantities of “dark data” embedded in unstructured PDF archives. In archaeology and heritage management, this challenge is amplified by decades of technical reports, stratigraphic records, regulatory interpretations, geospatial metadata, and catalogues comprising several million artifacts dispersed across sites. Standard Retrieval-Augmented Generation (RAG) systems relying solely on vector databases often struggle with structured, relational, chronological, or geospatially constrained queries [1]. This research proposes a hybrid architectural approach integrating large-scale PDF vectorization, dual (vector and relational) indexing, geospatial metadata integration, and parameter-efficient model alignment to ensure strict factual rounding and traceability.

(2) Partner activities and Challenges
Archéoconsultant operates in professional archaeology and territorial heritage management while also developing and commercializing digital solutions for the archaeological sector in Canada and internationally. The organization manages a substantial archive of domain-specific documentation essential for regulatory guidance, mitigation planning, and environmental impact assessments. The partner seeks to bridge the “trust gap” in AI adoption by developing a domain-constrained knowledge infrastructure capable of delivering verifiable, source-grounded responses while enabling spatially aware and chronologically precise analysis.

(3) Anticipated Social and Economic Benefits
The project converts days of manual research into near-instantaneous, high-precision queries, generating significant economic value by accelerating development timelines and reducing compliance risks for large-scale infrastructure. By exploiting structured data, the system strengthens governance through more transparent, traceable, and evidence-based regulatory decision-making. Ultimately, this scalable framework fosters social impact by enabling proactive cultural heritage protection and positioning the Canadian heritage sector as a leader in responsible, modern AI adoption.

Faculty Supervisor:

Elyes Manai

Student:

Partner:

Archéoconsultant Inc.

Discipline:

Computer science

Sector:

Professional, scientific and technical services

University:

Université du Québec à Chicoutimi

Program: