Glue Reply and Graymatter Reply developed a proof-of-concept, AI-powered inspection workflow that enables technicians to capture inspection data by voice, digitises job cards in real time, and integrates with a premium truck brand’s workshop systems to improve productivity, quality and cash flow.

The Challenge

Modernise inspection and job card processes without compromising quality or compliance.

For this premium truck brand, every vehicle inspection generates information that drives servicing, invoicing, warranty processing and customer communication. The quality and speed of this information directly impacts workshop performance and financial outcomes.

Traditional paper-based job cards and manual documentation create delays, duplicate effort and inconsistent data quality. Technicians spend valuable time completing paperwork, while costers and workshop teams spend additional time clarifying missing information before vehicles can be invoiced. The challenge was not simply digitising forms, it was capturing accurate, structured information at the point of work while maintaining compliance and supporting existing workshop processes.

The Scenario

Transforming inspection capture across a complex workshop environment.

The proof of concept focused on one of the most operationally intensive areas of the service process: vehicle inspections involving more than 100 inspection checkpoints and multiple downstream systems.

Technicians needed a simple hands-free way to capture inspection findings while working on the vehicle. Workshop teams required complete, high-quality job cards that reduced rework, while existing systems required structured information that could be processed without manual re-keying. The opportunity extended beyond inspections, creating the digital foundation for future automation across warranty, invoicing and workshop operations.

The Solution

AI-powered inspection capture and digital job cards.

Glue Reply and Graymatter Reply developed an AI-powered inspection solution that enables technicians to capture inspection findings using voice or manual input directly from the workshop floor. Speech is transcribed in real time, transformed into structured job card data and prepared for integration with the brand’s existing operational systems.

As inspection information is captured, technicians can review, amend and validate the generated output before submission. The solution improves data quality at source, reduces duplicate administration and provides cleaner, structured information for downstream workshop, costing and compliance processes.

How We Did It

Combining AI, voice technology and enterprise integration.

The solution combines speech recognition, large language models and enterprise integration to transform manual inspection processes into a structured digital workflow. AI extracts inspection findings from technician narration, maps information into the required job card structure and prepares validated outputs for existing workshop systems.

  • Voice-enabled inspection capture: Technicians capture inspection findings naturally using voice while remaining focused on the vehicle, reducing manual administration and duplicated data entry.
  • Intelligent document generation: AI transforms inspection conversations into structured digital job cards, improving consistency, completeness and overall documentation quality.
  • Enterprise system integration: Validated inspection data integrates with Autoline and R2C, reducing manual processing while creating a scalable digital foundation for future workflow automation.

The Result

Higher-quality inspection data with faster downstream processing.

The AI Inspect proof of concept demonstrated how inspection information can move through the workshop. Accurate job cards are produced earlier in the process, reducing rework, improving documentation quality and enabling faster progression towards invoicing and warranty processing.

By capturing structured information at source, the proof of concept demonstrated a digital workflow that can improve operational efficiency while creating the platform for future automation across service operations.

  • Higher-quality job cards: Accurate, structured job cards are produced earlier in the process, reducing rework and lifting overall documentation quality.
  • Faster downstream processing: Cleaner information at source speeds progression towards invoicing and warranty processing across the workshop.
  • Foundation for future automation: Capturing structured data at source improves operational efficiency and creates the platform for future automation across service operations.

What This Represents

AI Inspect is not a standalone tool, it is an operating model intervention. It targets a systemic bottleneck in service delivery by embedding AI directly into the workflow rather than layering it on afterwards. The technician’s process barely changes. The administrative process changes entirely.

As a proof of concept, its job was to prove the pattern: that structured, compliant information can be captured at the point of work, validated by the person doing the work, and handed to existing systems without a second pass. That is what makes the automation that follows possible.