research

Research grounded
in real operations.

Avantgard conducts original research at the intersection of enterprise AI, industrial knowledge systems, and operations management. Our work addresses the conditions that real deployments face — fragmented data, legacy infrastructure, governance requirements, and decisions with operational consequences.

3research areas
3papers (published & forthcoming)
3active research threads
research areas

Three interconnected
domains.

Industry 5.0 reframes the goals of industrial transformation — from pure efficiency and automation to a model where technology serves human capabilities, builds organisational resilience, and supports sustainable operations at enterprise scale.

Where Industry 4.0 asked "how do we automate this?", Industry 5.0 asks "how do we make this human-AI collaboration work reliably, at scale, under real operational constraints?" This requires rethinking operating models, knowledge structures, and AI governance from the ground up.

research threads

  • Collaboration architectures for human-AI decision-making in industrial settings
  • Operating model design for AI-augmented, people-centred enterprises
  • Governance frameworks for responsible AI deployment in operations
  • Transition pathways from automation-first to augmentation-first industrial systems

Most enterprise AI deployments underperform not because the models are wrong, but because the knowledge infrastructure is insufficient. Rules, relationships, constraints, and institutional logic remain locked in documents, systems, and people — inaccessible to AI at the moment a decision is needed..

Our research develops practical knowledge graph architectures that work in real enterprise conditions: imperfect data, legacy systems, limited semantic expertise, and governance requirements.

research threads

  • File-based knowledge graph architectures for practical enterprise adoption
  • Ontology governance and versioning in multi-team environments
  • RAG (Retrieval-Augmented Generation) grounded in structured enterprise KGs
  • Knowledge graph design patterns for manufacturing, construction, and retail
  • Provenance, traceability, and explainability in KG-backed AI systems

Operations systems are being redesigned from first principles. The question is no longer how to automate more tasks or layer AI onto existing workflows. It is how to architect systems where physical processes, digital intelligence, and human judgment are structurally integrated — with defined boundaries, reliable handoffs, and governance built into the design itself.

This domain addresses the engineering and organisational design of next-generation Human-Cyber-Physical Systems (HCPS): environments where the physical and the digital are tightly coupled, where human agency is preserved by design, and where operational decisions carry consequences that demand auditability and resilience.

Prefabricated & Panelised Construction

Construction 5.0 demands reconfigurable production systems where design intent, manufacturing logic, logistics coordination, and assembly execution must remain coherent across long, multi-stakeholder project lifecycles.

Smart Retail Operations

Retail intelligence requires synthesising demand signals, inventory positions, pricing rules, and supplier constraints in real time. Research focus: operational context layers that make AI pricing, allocation, and procurement decisions reliable and auditable.

Advanced Manufacturing

Deeply integrated production environments where quality, scheduling, knowledge management, and human oversight operate as a unified system rather than separate applications.

publications

Published &
forthcoming work.

Journal paperforthcoming

File-Based Knowledge Graphs and Retrieval-Augmented AI for Complex Project Delivery

abstract

Complex engineering and transformation projects generate heterogeneous knowledge that is difficult to integrate, trace, and reuse across multi-year lifecycles. Although knowledge graphs (KGs) and retrieval-augmented generation (RAG) have independently matured, many teams still lack a practical path from fragmented documents to explainable AI-assisted decision support. This paper develops an end-to-end design pattern for file-based project knowledge graphs: KGs whose canonical representation resides in structured files (Markdown + YAML + explicit links), rather than in dedicated graph databases. It is based on a prototype built for a real-case, multi-year, multi-site industrial MES implementation project, and presents a detailed design that covers ontology governance, graph encoding patterns, agentic retrieval loops, provenance rules, human-in-the-loop write controls, and production-oriented cost optimisation.

authors

Pedro Miguel Lourenço

Knowledge GraphsRAGComplex Project DeliveryMESOntologyAgentic AI

key contributions

01
File-based KG architecture
Markdown + YAML + explicit links as canonical graph representation
02
Agentic retrieval loops
RAG pipelines grounded in structured project knowledge graphs
03
Ontology governance
Versioned ontology management for multi-year project contexts
04
Provenance & explainability
Human-in-the-loop write controls and traceable AI decisions
05
Cost optimisation
Production-oriented strategies for sustainable enterprise RAG
Read the paper — link forthcoming
Conference paperin progress

Reconfigurable Production System for Prefabricated Panelised Construction

abstract

Prefabricated panelised construction offers significant efficiency gains but requires production systems that can adapt to multiple product variants without costly process redesign. Traditional fixed-sequence production lines struggle with design variability, leading to bottlenecks and rework. This paper presents a reconfigurable production architecture that maintains production coherence across variant design spaces while coordinating fabrication sequences, logistics constraints, and multi-site assembly workflows. The approach is grounded in a real industrial deployment and demonstrates how structured ontology-driven production planning can enable factory-level flexibility without sacrificing traceability or quality control.

authors

Pedro Miguel Lourenço

Prefabricated ConstructionReconfigurable ProductionIndustry 5.0Smart Manufacturing

key contributions

01
Reconfigurable architecture
Design framework for production systems that adapt to multiple product variants without process redesign
02
Production-logistics integration
Methods to maintain coherence between design intent, manufacturing sequences, and logistics constraints
03
Multi-site coordination
Governance model for coordinating fabrication, assembly, and site delivery across distributed teams
04
Knowledge traceability
Systems approach to capturing and reusing production knowledge across project lifecycle
Read the paper — link forthcoming
Journal paperforthcoming

Anatomic Taxonomy-Based Medical Element Recovery from Speech-to-Text AI Transcripts in Radiology Reporting

abstract

Radiology reporting remains a critical bottleneck in diagnostic imaging workflows. While speech-to-text technology has dramatically accelerated dictation, converting unstructured narrative transcripts into queryable clinical data remains manual and error-prone. Transcription errors, anatomic terminology variation, and spatial relationship ambiguity further complicate automated extraction. This paper develops a taxonomy-driven framework for recovering structured medical elements from speech-to-text radiology transcripts by grounding language understanding in formal anatomic ontologies and spatial relationship models. The approach integrates medical NLP with standardised clinical taxonomies (SNOMED, RadLex) and demonstrates how controlled vocabulary recovery can enable reliable downstream tasks—from quality assurance to evidence extraction to epidemiological analysis—without requiring manual correction of transcript errors.

authors

Pedro Miguel Lourenço

RadiologySpeech-to-TextClinical OntologyAnatomic TaxonomyMedical NLPHealthcare AI

key contributions

01
Anatomic taxonomy framework
Structured representation of anatomical concepts and spatial relationships for reliable entity extraction
02
Speech-to-text reliability
Methods to handle transcription errors and clinical terminology variation in radiology narratives
03
Clinical ontology integration
Mapping mechanisms between speech outputs and standardized medical taxonomies (SNOMED, RadLex)
04
Structured data recovery
Conversion from natural language radiology reports to queryable, auditable clinical data structures
Read the paper — link forthcoming
more forthcoming
approach

What
“research-based”
means here.

01

Primary research

Original design patterns, prototypes, and frameworks developed from real project contexts — not retrospective literature reviews.

02

Applied prototyping

Research ideas are validated through working prototypes built on real operational environments, not toy examples.

03

Industry validation

Findings are tested against the constraints of actual enterprise deployments — messy data, legacy systems, real governance requirements.

04

Open publication

We publish our findings and make the work available to the broader community of practitioners and researchers.

research notes

Thinking in
progress.

Ideas and threads being explored — not yet ready for publication but worth documenting. We'll publish proper notes here as they mature.

○ exploring

Ontology alignment patterns for manufacturing ERP integration

Exploring practical design patterns for mapping ERP data structures (SAP, Oracle) to domain ontologies without full schema migration.

note pending
● active

Human-AI decision handoff models in industrial operations

When should an AI agent escalate to a human? Formalising the conditions, triggers, and interface patterns for reliable human-in-the-loop operation.

note pending
● active

Cost optimisation strategies for enterprise RAG pipelines

Practical techniques for reducing inference costs in production RAG systems without sacrificing retrieval quality or explainability.

note pending

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Interested in the research?

We're open to collaborations with industry partners, academic institutions, and practitioners working in the same domains.

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