Domain-Specific AI for Infrastructure

Turn Project Documentation
Into Answers

Infralytics transforms thousands of PDFs, BIM models, and spreadsheets into a unified knowledge space. Ask a question. Get a cited answer in seconds.

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See How It Works

Validated on real infrastructure project data

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The Problem

Infrastructure Runs on Documents.
Nobody Can Find What They Need.

0

per week spent searching for project data by the average engineer

FMI / PlanGrid Study

0

of project value lost to rework caused by outdated or missing information

Construction Industry Institute

0

of global GDP is construction — the least digitalized major industry

McKinsey Global Institute

Every infrastructure project generates thousands of documents across dozens of formats. Engineers spend more time finding information than using it. Critical discrepancies between plans, models, and reports go undetected until they become costly rework on site.

The Solution

One Question. One Verified Answer.

infralytics query engine

Query

Capabilities

Query. Compare. Detect.

Intelligent Querying

Ask questions in natural language across your entire project documentation. Every answer comes with precise citations and source references you can verify.

Cross-Source Comparison

Automatically compare data across PDFs, IFC models, and Excel files. Identify what matches, what doesn't, and what's missing — across formats and versions.

Discrepancy Detection

Automatically surface inconsistencies between documents, models, and spreadsheets. In a real-world infrastructure project validation, 650+ discrepancies were automatically detected.

How It Works

Three Steps to Clarity

Dedicated data pipelines transform raw project files into a structured, indexed knowledge base that an AI agent queries with domain-specific tools.

1

Ingest

Dedicated ingress pipelines process each file type with format-specific logic — no generic document converters. Every pipeline extracts structure, metadata, and relationships that general-purpose tools miss.

PDF Pipeline

Hybrid text extraction — native for digital PDFs, OCR for scanned documents
Visual LLM annotation of technical drawings — tables, legends, dimension lines, symbols
Context-aware chunking that preserves chapters, headings, and drawing references

IFC / BIM Pipeline

Full extraction of elements, PropertySets, QuantitySets, classifications, and spatial relations
Indexed with nested PropertySets for queries like "all elements with ConcreteClass=C30/37 and Volume > 30 m³"
Pre-computed aggregations — total volumes by type and segment in milliseconds

Excel Pipeline

Schema-driven approach — auto-generates schema (sheets, columns, types, semantics) before querying
Rich extraction: merged cells, formulas, conditional formatting, comments, styling
Indexed for fast aggregate queries across segments, element types, and zones

2

Understand

The AI agent doesn't just search — it reasons with specialized tools. Each MCP (Model Context Protocol) server provides a structured interface to a specific data domain, combining semantic and structured queries.

RAG Search

Hybrid semantic + keyword search across all documentation. Natural language queries matched against vectorized content with metadata filters by document type, section, or zone.

Structured Search & Aggregations

Full-text and filtered search over indexed data with aggregate queries — counts by type, volume sums by segment, averages, min/max — with full traceability to source.

IFC Query & Analysis

Search elements by classification, location, and properties. Aggregate volumes, counts, and dimensions across any filter combination. Batch queries for large element sets.

Recipe Store

Stores successful query patterns — tools used, parameters, script sequences — and matches new questions via semantic similarity for faster, more reliable repeat answers.

3

Answer

Every response is cited and verifiable. Click any reference to jump to the exact page in a PDF, element in an IFC model, or row in a spreadsheet. Answers can be exported as reports, CSV tables, or BIM viewpoints.

Cited Sources

Every claim traces to a document, page, section, or model element

Reference Viewer

Click a citation to see the PDF page, IFC properties, or Excel row in context

Export & Render

Generate PDF reports, CSV tables, or BCF viewpoints for BIM navigation

Supported formats PDF IFC / BIM Excel DOCX DWG

Validated

Tested on Real Infrastructure Projects

Cross-Border Tunnel — Second Tube

Real-world infrastructure mega-project

0

Files analyzed

0

Total data

0 + 0

PDFs + BIM Models

0

Discrepancies found

"Volume cross-validation: 140 tunnel segments, difference of 0.000 m³ — perfect consistency between the IFC model and engineering calculations."

iC consulenten

Letter of Intent

International engineering firm with 700+ engineers across 10 countries. Committed to piloting Infralytics on active infrastructure projects.

ODE (Open Digital Engineering)

Letter of Intent

Leading digital engineering consultancy. Partnering with Infralytics to integrate AI-powered document intelligence into BIM workflows.

The Team

Infrastructure Expertise
Meets AI Engineering

30+ years of infrastructure. 20+ years of production ML. One team.

Žiga Vehovec

CEO

Civil engineer, BIMA+. 10+ years in infrastructure project management and BIM coordination.

Vid Klopčič

CTO

Mathematics & physics background. ML systems architect. GZS Gold Award for Innovation 2025.

Matevž Pešek

Advisor

Associate Professor, University of Ljubljana. 15+ peer-reviewed publications in AI and multimedia analysis.

Marko Žibert

Infrastructure Lead

20+ years in infrastructure engineering. Former director at Elea iC. Deep domain expertise in tunnels and bridges.

Ready to Stop Searching
and Start Finding?

We're onboarding early partners for pilot projects. Let's explore what Infralytics can do with your project data.