If you're managing logistics for an international EPC contractor, you know the reality: thousands of invoices in different formats, product specifications in multiple languages, and teams spending entire days extracting and standardizing data that should be flowing seamlessly through your systems.

The irony? While the AEC industry has embraced digital transformation in design and project management, the logistics backbone - the flow of material data, specifications, and documentation - often remains stubbornly manual. The result isn't just inefficiency. It's missed opportunities, delayed shipments, customs complications, and teams buried under repetitive tasks when they should be driving strategic value.

The problem

For international material suppliers serving major AEC firms, the data management challenge grows exponentially with scale. Consider what happens when your company sources from dozens of manufacturers across different countries:

• Invoices arrive in countless templates, formats, and languages depending on the issuing company, currency, and country.

• Product specifications must be extracted, translated, and standardized before they can be used.

• Technical sheets and certificates need to be verified against customs regulations for international shipments.

• Teams spend hours and sometimes days manually extracting data from PDFs, reconciling inconsistencies, and preparing documents for the next stage in their workflow.

One of our clients, a leading EPC contractor, quantified this challenge: their workforce was spending an enormous amount of time just extracting data from incoming documents. Multiple translators were required to handle the language diversity, and even then, bottlenecks were constant. The question wasn't whether they needed a better system - it was whether a better system was even possible given the complexity.

Most companies respond to growing document volumes by hiring more staff. More data entry clerks. More translators. More quality control reviewers. But this approach doesn't scale; it multiplies:

• Inconsistency: Different team members interpret and format data differently

• Bottlenecks: Translation needs create delays that ripple through the entire supply chain

• Errors: Manual data entry introduces mistakes that compound downstream

• Cost: Labor costs grow linearly (or worse) with document volume

• Complexity: With more suppliers operating internationally, aligning specifications with local regulations becomes a complex task

• Inflexibility: Expanding to new markets or suppliers means hiring specialized staff for each new language or document type

As one procurement manager put it during our discovery:

“We are spending more time verifying invoices and certificates than actually managing suppliers.”

Ultimately, legacy document management systems offer storage and search, but they don't solve the fundamental problem: transforming unstructured, inconsistent documents into standardized, actionable data.

The AECFoundry approach

Working closely with our client, we developed a custom, centralized platform that addresses the core challenge: transforming the chaos of incoming documents into a standardized, searchable, actionable asset. Creating a platform that solves document chaos requires rigorous methodology and systematic validation. We had 2 weeks for discovery and 6 weeks for development to bring the solution to life.

Discovery and requirements 

Our team began with continuous discovery in our client’s reality. They explained their day-to-day workflows and provided us with sample invoices, specifications, and technical documentation from suppliers across different countries, each with unique formats, structures, and languages. This was not a curated dataset; it was the messy, real-world document our client faces daily.

Similarly, we have got involved in their end-to-end workflow to better understand where our solution could bring more value.

Creating evals

To ensure our AI system delivered reliable results, we built a comprehensive evaluation dataset from ground truth data. Working with the client's historical database, we extracted documents that had been manually processed and verified by their team over time. Each document in this dataset included the original PDF alongside the correctly extracted and formatted data that human operators had entered into their system.

This ground truth dataset became the foundation for measuring accuracy. For each field we needed to extract - product names, specifications, quantities, supplier details, and technical parameters - we had verified examples of what correct extraction looked like. We could then test any AI model against this benchmark and measure precisely how often it matched the human-verified data.

The evaluation dataset represented the real-world complexity our system would face: invoices in multiple formats and languages, technical specifications with varying structures, and product sheets with inconsistent terminology. By testing against this diverse, verified dataset, we could confidently assess which AI approaches would perform reliably in production.

Benchmark development and Model Evaluation 

Before building anything, we needed to know which AI models could actually handle the intended task. We developed a rigorous evaluation framework starting with ground truth creation - gathering manually extracted and translated data from sample invoices and specifications to create a verified benchmark dataset. 

We evaluated the best available commercial AI models and document intelligence workflows in parallel. This included leading large language models with vision capabilities, specialized document AI services from major cloud providers, and purpose-built document processing platforms. All of them were  tested using our ground truth dataset, measuring how good each model was in extracting each relevant field, processing speed per document, and estimated API costs at production scale.

The results were revealing. Some models excelled at structured invoices but failed on technical specifications and tables. Others showed high accuracy but prohibitive costs at scale. We selected a well-rounded performer that balanced accuracy, speed, and cost.

Production Engineering & User Experience 

Choosing the right model was just the beginning. Productionizing the end-to-end pipeline requires robust engineering beyond model selection.

We developed an intuitive interface that balances automation with human oversight. The system processes documents in seconds while presenting extracted data in a clear, reviewable format. Users can quickly verify, correct, and approve extracted information before it flows into downstream systems. This human-in-the-loop design ensures accuracy while eliminating repetitive manual data entry.

Behind the interface, we implemented comprehensive LLMOps practices including detailed tracing and monitoring of both accuracy metrics and error patterns. Every extraction is logged, allowing us to analyze failure modes systematically. Since we analyze errors carefully, we know exactly which document types, which producers, and which specific fields the system struggles with. This error analysis drives continuous improvement - we can prioritize engineering work based on real performance data rather than guesswork.

Critically, this traceability made it possible to verify that we met the client's 90% accuracy threshold for each extracted field. Without systematic monitoring, quantifying system reliability at this granular level would be practically impossible. The metrics aren't just numbers - they represent confidence that the system performs consistently in production.

Result

A centralized system that processes documents in seconds, maintains high accuracy through human-in-the-loop collaboration, and integrates naturally into existing workflows.

AI-Powered Document Recognition

Upload an invoice, product sheet, or specification document, and the platform will immediately:

• Recognizes document structure regardless of format

• Extracts key information using advanced AI techniques

• Standardizes data into your preferred format

• Presents extracted fields for human review and approval

The "human-in-the-loop" design ensures accuracy while AI does the heavy lifting and eliminates repetitive work. Reviewers can verify extracted fields, approve, or edit them before finalizing entries, ensuring accuracy where it matters most.

Multilingual Translation at Scale

Our client drastically reduced their reliance on multiple external language specialists. The platform handles translation across a wide range of languages, preserving technical accuracy and high-fidelity translations while enabling rapid document processing.

This doesn't replace human expertise - it amplifies it. Complex nuances still get human review, but the bulk of translation work happens instantly, removing the bottleneck that used to delay every international transaction.

Seamless ERP Integration

Extracted and standardized information flows directly into existing ERP systems. No export-import cycles. No manual data transfer. The platform becomes the intelligent bridge between your incoming documents and your business systems.

Measured Impact: From Efficiency to Strategy

The outcomes go far beyond "faster document processing". Our client experienced transformation across multiple dimensions:

Accuracy and Reliability

The system achieved over 90% accuracy for each extracted field and entity type, meeting the reliability threshold that makes automated processing viable. This consistency means teams can trust the extracted data for downstream operations with minimal manual verification.

Processing Speed

Extraction speed increased 5x compared to manual operations, particularly dramatic for documents with extensive tables. What previously took hours of manual data entry now completes in minutes, freeing staff to focus on higher-value verification and analysis work.

Strategic Capabilities

Beyond efficiency gains, structured data creates new strategic capabilities. Information becomes searchable, analyzable, and actionable in ways scattered PDFs never could. The foundation exists for advanced analytics, machine learning-based optimization, and automated workflows that were previously impossible.

Beyond Material Supply: Wider Applications in AEC

What we built for logistics solves a broader challenge across the AEC industry: transforming chaotic, multi-format, multilingual documents into standardized, actionable data.

Contractors managing building permits face nearly identical challenges - reformatting consultant documentation to meet different municipal requirements. Procurement departments process CE markings, EPDs, and technical data sheets from hundreds of suppliers, extracting compliance data and sustainability metrics. Façade subcontractors deal with invoice variability and specification revisions that must align with the bill of quantities. Modular manufacturers normalize multi-factory invoices and manage customs documentation. MEP equipment importers handle strict HS codes and certificates, where messy invoices cause expensive delays.

The commonality? Document chaos that slows operations, creates bottlenecks, and prevents teams from focusing on higher-value work. The solution we developed isn't just for logistics - it's a template for document management challenges wherever they appear in the AEC value chain.

The Path Forward

This case study focused on PDF document extraction - invoices, specifications, and certificates. The same approach of extracting and organising unstructured data extends naturally to other document types that plague the AEC industry: technical drawings, site photos, equipment manuals, and visual inspections.

The platform we built for logistics proves that document chaos - can be systematically transformed into structured, actionable intelligence. Digital transformation in AEC isn't about replacing people with technology. It's about freeing experts from tedious data wrangling so they can focus on the engineering, coordination, and decision-making that actually creates value.

Our human-in-the-loop design ensures that engineers, project managers, and supply chain experts remain at the center of every decision. This balance of automation and human judgment is what makes adoption successful and sustainable in an industry built on trust, safety, and precision. 

The platform we built for our client proves that the chaos of multi-format, multilingual document management is solvable. More importantly, it demonstrates that solving this problem unlocks capabilities that weren't possible before: real-time insights, predictive analytics, and the agility to scale without proportional cost increases.

Ready to Transform Your Document Management?

If your team is spending too much time extracting unstructured data and not enough time creating value, let's talk. 

Book a 45-minute product value workshop with our team.