One of the biggest challenges in the hair extension industry is helping customers find the right colour to match their natural hair. Online colour matching is difficult due to differences in screen settings, lighting, and image quality, making it hard for customers to accurately judge shades before purchasing.
These mismatches often lead to dissatisfaction, returns, and exchanges, causing delays for customers and financial losses for businesses. In addition, unhappy customers may leave negative reviews, which can seriously damage brand reputation and reduce customer trust.
GlobalMatch
GlobalMatch is an AI-powered virtual try-on and colour-matching platform for online retailers and sellers worldwide. It enables customers to virtually try on wearable products — including clothing, shoes, glasses, and accessories — and see how different colours, such as hair colour, match with products in your online store. By removing guesswork and reducing mismatches, GlobalMatch helps improve purchase confidence and create a more engaging shopping experience.
HTR Hair Colour Match
This app enables customers to match their natural hair colour with hair extension products from multiple online retailers in one place. Users can compare brands and stores based on their preferences, while hair extension companies can list their shops, compete for premium placement, and promote products through targeted advertising.
Featured Project
AI-driven Chatbot
An AI-driven chatbot serves as a central, intelligent interface between a business and its customers, employees, or partners. It enables natural, conversational interactions that allow users to ask questions and receive assistance in real time, without needing to navigate complex systems or wait for human support. Available 24/7, the chatbot ensures consistent communication and faster responses across multiple channels, helping organisations improve engagement and service quality.
By integrating internal business data (such as knowledge bases, CRM systems, policies, or transaction records) with trusted external information, the chatbot delivers accurate, context-aware answers tailored to each user’s needs. This reduces reliance on manual lookups and repetitive human intervention, while ensuring information remains up to date and reliable. The result is a smarter, more responsive experience for users.
For businesses, this translates into lower operational costs, increased efficiency, and better scalability. Routine queries and tasks are handled automatically, allowing teams to focus on higher-value work. At the same time, insights from chatbot interactions help organisations understand customer needs, identify gaps in information, and continuously improve products, services, and decision-making.
Here’s what to expect from the design process
1. Problem Understanding & AI Framing
Our design process begins with a deep understanding of the problem context, operational environment, and business objectives. We work closely with stakeholders to identify where AI can deliver meaningful impact, distinguishing between tasks best handled by automation, decision-support, or intelligent agents.
This stage includes defining data availability, system constraints, risk tolerance, and success criteria, ensuring that AI is applied purposefully rather than as a generic solution.
2. AI System Design & Architecture
Based on the problem framing, we design an AI system architecture that aligns with real-world operational requirements. This may involve selecting and orchestrating technologies such as large language models, computer vision pipelines, or agent-based workflows.
Design decisions focus on robustness, scalability, explainability, and integration with existing systems. We prioritise architectures that support monitoring, human oversight, and controlled behaviour in production environments.
3. Prototyping, Evaluation & Iteration
We develop prototypes and early implementations to validate assumptions and evaluate performance under realistic conditions. This iterative phase allows us to test model behaviour, system interactions, and user experience, refining the solution based on feedback and observed results.
Evaluation goes beyond accuracy alone, considering reliability, usability, and operational fit. Insights gained during this stage guide refinement before full deployment.
4. Deployment, Governance & Continuous Improvement
Once validated, the solution is prepared for deployment within live environments. We support integration, documentation, and handover, ensuring the system can be operated, maintained, and governed effectively.
AI systems evolve over time, and our process includes ongoing review and improvement. We emphasise responsible AI practices, including performance monitoring, risk management, and adaptation as data, requirements, or operational contexts change.
FAQs
What my clients are saying
“A Valuable Tool for Accurate Online Shade Selection”
Our customers love the Hair Colour Match app! Since integrating it into our online store, we’ve seen a significant reduction in returns and far fewer colour-related enquiries. The tool makes it easy for shoppers to find the perfect extension shade by matching their natural hair colour with products from our brands in seconds. It’s intuitive, accurate, and has boosted both customer confidence and sales.
Kevin
Hair100
“Easy and Accurate”
The Hair Colour Match app made choosing the right extension shade so much easier! I just uploaded a photo, and within seconds it matched my natural hair colour to the closest options from multiple brands. The recommendations were accurate and gave me confidence to buy online — no more guessing or returns. I love that it shows products from different stores in one place, and the whole experience felt fast, fun, and reliable.
Louise Smith
Customer
“A pleasure to work with”
CognivisAI delivered practical, reliable AI solutions that integrated smoothly with our workflows and produced measurable business value.
Anny White
Happyqueen
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