Client Stories | Real‑World AI Business Impact

GenAIQ automates complex workflows by cutting manual effort, accelerating decisions, and generating real ROI from day one.

Automotive manufacturing
From documentation overload to 10% faster product development

A global family-owned manufacturer with 3,500+ employees across 13 production sites used GenAIQ to automate the evaluation of technical documents, cutting review cycles, eliminating inconsistencies, and accelerating time-to-market.

The challenge
- Constant manual review of specifications, test reports, and engineering drawings
- Mix of plain text, sketches, images, and tables in source documents
- High operational cost from repetitive evaluation steps
- Risk of human error and slow turnaround across document versions
The GenAIQ solution

Automated agent workflows for end-to-end document evaluation

Conversion of technical drawings into structured, analyzable text

Automated requirement catalog comparison

One-click extraction of test metrics into tables

Unified output formats for downstream engineering tools
- Faster requirement comparison
- Less time on drawing reviews
- Efficiency gain in test procedures
Fiber network construction
143% ROI in year one: Automating low-level design validation

A major fiber-network construction company transformed its design quality and project delivery speed by automating LLD validation, surface checks, and documentation workflows directly inside their existing QGIS environment.

The challenge
- Manual validation of LLDs and construction paths was slow and error-prone
- Multiple rule checks - surface types, permits, geodata, caused repetitive work
- Only partial validations possible due to time and cost constraints
- Unstructured project folders slowed onboarding and reduced team transparency
The GenAIQ solution

Automated detection, classification, and documentation of LLD issues inside QGIS

AI-driven surface checks, path validation, and geodata sanity checks

Issue-management interface for fast review and resolution of findings

Automated generation of standardized PDF reports for engineering teams

AI-powered file organizer that structures project folders by predefined rules
- Efficiency gain from automation
- Lower operational costs
- Higher design accuracy
Telecommunications
30% efficiency gain in HLD cost controlling for a leading telecom operator

A leading telecom operator with extensive network rollout activities automated its high-level design cost analysis—eliminating fragmented manual workflows and giving engineers one-click access to accurate cost reports.

The challenge
- Cost-critical data scattered across many disconnected systems
- Manual, repetitive evaluation steps slowing down engineering teams
- Frequent design changes made traditional automation approaches hard to scale
- Limited transparency into cost components and underlying data
The GenAIQ solution

Automated extraction and analysis of design-related cost information

OCR-based table extraction and mathematical preprocessing for accuracy

Graph-based workflow engine that adapts quickly to changing inputs

One-click process from document ingestion to ready-to-use cost report

Configurable presets for consistent results, even for non-technical users
- Fewer manual touchpoints
- Increased cost transparency
- Higher accuracy
Telecommunications — Process transformation
25% faster process optimization through AI-powered BPMN analysis

The same leading telecom operator tackled a second challenge: analyzing a complex landscape of BPMN process files to identify optimization opportunities, without the months of manual effort typically required.

The challenge
- BPMN process files contained syntax errors limiting automated evaluation
- Manually identifying optimization opportunities was slow and inconsistent
- Difficult to derive state-of-the-art solutions from process analysis alone
- Standard LLM tools lacked ability to research current industry best practices
The GenAIQ solution

Batch upload of multiple BPMN process charts for simultaneous analysis

Automatic detection and surfacing of BPMN syntax errors

First AI agent analyzes charts and researches state-of-the-art solutions

Second agent derives optimization potentials based on the first agent's findings

Structured Excel output with all identified optimization opportunities
- Faster process optimization cycles
- Less manual analysis & research
- Transparency of cost‑driving factors