Rushden, NN10 6BS,
United Kingdom
Autoglass BodyRepair Technographics
Discover the latest software purchases and digital transformation initiatives being undertaken by Autoglass BodyRepair and its business and technology executives. Each quarter our research team identifies on-prem and cloud applications that are being used by the 250 Autoglass BodyRepair employees from the public (Press Releases, Customer References, Testimonials, Case Studies and Success Stories) and proprietary sources.
During our research, we have identified that Autoglass BodyRepair has purchased the following applications: Microsoft Azure Cloud Services for Application Hosting and Computing Services in 2019, Microsoft Azure Monitor for Application Performance Management in 2021, IBM Watson Visual Recognition for Computer Vision in 2017 and the related IT decision-makers and key stakeholders.
Our database provides customer insight and contextual information on which enterprise applications and software systems Autoglass BodyRepair is running and its propensity to invest more and deepen its relationship with Microsoft , IBM , Cloudflare or identify new suppliers as part of their overall Digital and IT transformation projects to stay competitive, fend off threats from disruptive forces, or comply with internal mandates to improve overall enterprise efficiency.
We have been analyzing Autoglass BodyRepair revenues, which have grown to $16.0 million in 2024, plus its IT budget and roadmap, cloud software purchases, aggregating massive amounts of data points that form the basis of our forecast assumptions for Autoglass BodyRepair intention to invest in emerging technologies such as AI, Machine Learning, IoT, Blockchain, Autonomous Database or in cloud-based ERP, HCM, CRM, EPM, Procurement or Treasury applications.
IaaS
Vendor |
Previous System |
Application |
Category |
Market |
VAR/SI |
When |
Live |
Insight |
Insight Source |
|---|---|---|---|---|---|---|---|---|---|
| Microsoft | Legacy | Microsoft Azure Cloud Services | Application Hosting and Computing Services | IaaS | n/a | 2019 | 2019 | ||
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Content Delivery Network | IaaS |
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2017 | 2017 |
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Content Delivery Network | IaaS |
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2020 | 2020 |
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ITSM
Vendor |
Previous System |
Application |
Category |
Market |
VAR/SI |
When |
Live |
Insight |
Insight Source |
|---|---|---|---|---|---|---|---|---|---|
| Microsoft | Legacy | Microsoft Azure Monitor | Application Performance Management | ITSM | n/a | 2021 | 2021 |
AI-Powered Application
Vendor |
Previous System |
Application |
Category |
Market |
VAR/SI |
When |
Live |
Insight |
Insight Source |
|---|---|---|---|---|---|---|---|---|---|
| IBM | Legacy | IBM Watson Visual Recognition | Computer Vision | AI-Powered Application | n/a | 2017 | 2017 | In 2017, Autoglass BodyRepair implemented IBM Watson Visual Recognition to assess vehicle damage and generate customer quotes, leveraging Computer Vision capabilities on its website. Customers upload images of vehicle damage and enter basic vehicle and contact details, the Watson service evaluates eligibility and issues a quote when it can, and customers may then book a service time and enter credit card details to facilitate payment after repairs are completed. The IBM Watson Visual Recognition deployment uses a curated image library of roughly 2,000 images to analyze and organize customer photos using four classifiers, type of vehicle, mobile repairable, product code and technician. Classifier outputs are mapped to repair cost determination logic and to product code identification, enabling automated quote calculation and photo triage as part of the intake workflow. Core functional capabilities implemented include image classification, automated quote generation, and classification-driven routing. Architecturally the solution is centered on a web-based image upload workflow that invokes IBM Watson Visual Recognition for inference, returning classification results that populate quote fields and booking options. Operational coverage is customer-facing intake on the Autoglass BodyRepair website and downstream use by scheduling and technician assignment teams operating in the United Kingdom. Payment capture is handled via customer-provided credit card details entered through the website after repairs are performed. Governance and process design embed an automated triage plus manual escalation pattern, when the Watson service cannot calculate a quote the company contacts the customer and determines costs through the normal manual process. The four classifiers act as decision gates for routing submissions into automated quoting or manual review, supporting parts identification and technician allocation based on product code and technician classifiers. The implementation centers on Computer Vision driven intake supporting quoting, scheduling and technician allocation workflows. |
CRM
Vendor |
Previous System |
Application |
Category |
Market |
VAR/SI |
When |
Live |
Insight |
Insight Source |
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Customer Experience | CRM |
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2017 | 2017 |
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Tag Management | CRM |
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2018 | 2018 |
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CyberSecurity
Vendor |
Previous System |
Application |
Category |
Market |
VAR/SI |
When |
Live |
Insight |
Insight Source |
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Secure Email Gateways (SEGs) | CyberSecurity |
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2017 | 2017 |
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PaaS
Vendor |
Previous System |
Application |
Category |
Market |
VAR/SI |
When |
Live |
Insight |
Insight Source |
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Transactional Email | PaaS |
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2019 | 2019 |
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