Visual Computing.
Engineered for Performance.

We design and optimise advanced computer vision, AI, and GPU‑accelerated solutions, turning complex ideas into scalable, high‑performance systems for real‑world impact.

2019
Founded in
95%+
Client Satisfaction Rate
20+
Successful Projects Delivered

What We Do

We specialise in guiding clients through the entire research and development journey, from initial prototyping to seamless integration and even safeguarding intellectual property. As an innovative solutions center, we not only identify areas for workflow enhancement but also actively engage in crafting and implementing solutions.

Industries

Life Sciences

Life Sciences

Visual Computing for Life Sciences

Surveillance

Surveillance

Privacy‑First Surveillance AI

Telecommunications

Telecommunications

Monetise the 5G Edge

Retail

Retail

AI-Powered Retail Innovation

Broadcast

Broadcast

Accelerating Connectivity

Why Choose Us?

We're not just your tech team — we're your thought partner. Every collaboration begins with deep understanding, followed by sharp execution.

Classical Vision

We offer expertise in foundational computer vision techniques to deliver versatile and performance-optimised solutions.

Explainability

Transparency matters. Our solutions prioritise explainability, catering to markets with stringent legal and ethical requirements.

Cross-Disciplinary

Our peripheral knowledge across various fields enhances your projects with unique, cross-disciplinary insight s for innovative solutions.

Scalable Solutions

We craft solutions with scalability in mind, combining optimisation, adaptability, and multi-GPU support for robust performance.

Frictionless Onboarding

We specialise in designing systems that streamline onboarding processes, thereby reducing costs and minimising time-to-adoption for your teams and workflows.

Multi-GPU Optimisation

Reduce cloud processing expenses with our expertise in multi-GPU optimisation, designed to handle demanding workloads efficiently.

ComputerVision

Who We Are

Look Beyond The Frame

We are a team of engineers, researchers, and creatives driven by a shared passion for visual computing and high performance. With roots in deep tech innovation, we help companies create computer vision and immersive solutions, with or without AI.

Meet the team Let's see
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Client Testimonials

Articles

Why Most Enterprise AI Projects Fail — and How to Predict Which Ones Will

Why Most Enterprise AI Projects Fail — and How to Predict Which Ones Will

22/04/2026

Enterprise AI projects fail at 60–80% rates. Failures cluster around data readiness, unclear success criteria, and integration underestimation.

What Types of Generative AI Models Exist Beyond LLMs

What Types of Generative AI Models Exist Beyond LLMs

22/04/2026

LLMs dominate GenAI, but diffusion models, GANs, VAEs, and neural codecs handle image, audio, video, and 3D generation with different architectures.

How to Architect a Modular Computer Vision Pipeline for Production Reliability

How to Architect a Modular Computer Vision Pipeline for Production Reliability

22/04/2026

A production CV pipeline is a system architecture problem, not a model accuracy problem. Modular design enables debugging and component-level maintenance.

How to Profile GPU Kernels to Find the Real Bottleneck

How to Profile GPU Kernels to Find the Real Bottleneck

22/04/2026

GPU profiling separates compute-bound from memory-bound kernels. Nsight Compute roofline analysis shows where a kernel sits and what would move it.

Proven AI Use Cases in Pharmaceutical Manufacturing Today

Proven AI Use Cases in Pharmaceutical Manufacturing Today

22/04/2026

Pharma manufacturing AI is deployable now — process control, visual inspection, deviation triage. The approach is assessment-first, not technology-first.

Why Generative AI Projects Fail Before They Launch

Why Generative AI Projects Fail Before They Launch

21/04/2026

GenAI project failures cluster around scope inflation, evaluation gaps, and integration underestimation. The patterns are predictable and preventable.

The Hidden Cost of GPU Underutilisation

The Hidden Cost of GPU Underutilisation

21/04/2026

Most GPU workloads use 30–50% of available compute. Without profiling, the waste is invisible. Bandwidth, occupancy, and serialisation are the root causes.

Machine Vision vs Computer Vision: Choosing the Right Inspection Approach for Manufacturing

Machine Vision vs Computer Vision: Choosing the Right Inspection Approach for Manufacturing

21/04/2026

Machine vision is deterministic and auditable. Computer vision is adaptive and generalisable. The choice depends on defect complexity, not preference.

What GxP Compliance Actually Requires for AI Software in Pharmaceutical Manufacturing

What GxP Compliance Actually Requires for AI Software in Pharmaceutical Manufacturing

21/04/2026

GxP applies to AI software that affects product quality, safety, or data integrity — not to every system in a pharma facility. The boundary matters.

The Real Cost of Pharmaceutical Batch Failure and How AI Prevents It

The Real Cost of Pharmaceutical Batch Failure and How AI Prevents It

21/04/2026

Pharmaceutical batch failures cost waste, rework, and regulatory exposure. AI-based process control prevents the failure classes behind most rejections.

How to Evaluate GenAI Use Case Feasibility Before You Build

How to Evaluate GenAI Use Case Feasibility Before You Build

20/04/2026

Most GenAI use cases fail at feasibility, not implementation. Assess data, accuracy tolerance, and integration complexity before building.

CUDA vs OpenCL vs SYCL: Choosing a GPU Compute API

CUDA vs OpenCL vs SYCL: Choosing a GPU Compute API

20/04/2026

CUDA delivers the deepest optimisation on NVIDIA hardware. OpenCL and SYCL offer portability. Choose based on lock-in tolerance and performance needs.

News

Generative AI Is Rewriting Creative Work

Generative AI Is Rewriting Creative Work

5/02/2026

Learn how generative AI reshapes creative work, from text based content creation and image generation to customer service and medical image review, while keeping quality, ethics, and human craft at the centre.

Cracking the Mystery of AI’s Black Box

Cracking the Mystery of AI’s Black Box

4/02/2026

A guide to the AI black box problem, why it matters, how it affects real-world systems, and what organisations can do to manage it.

Smarter Checks for AI Detection Accuracy

Smarter Checks for AI Detection Accuracy

2/02/2026

A clear guide to AI detectors, why they matter, how they relate to generative AI and modern writing, and how TechnoLynx supports responsible and high‑quality content practices.

Machine Learning on the Edge: Fast Decisions, Less Delay

Machine Learning on the Edge: Fast Decisions, Less Delay

30/01/2026

Learn how edge learning reduces delay, limits data transfer, and supports safer services by analysing data close to where it is created.

AI-Powered Customer Service That Feels Human

AI-Powered Customer Service That Feels Human

29/01/2026

Learn how artificial intelligence boosts customer service across chat, email, and social media with simple workflows, smart routing, and clear guidance, while keeping humans in charge. See how TechnoLynx offers practical solutions that lift quality, speed, and trust.

TPU vs GPU: Which Is Better for Deep Learning?

TPU vs GPU: Which Is Better for Deep Learning?

26/01/2026

A practical comparison of TPUs and GPUs for deep learning workloads, covering performance, architecture, cost, scalability, and real‑world training and inference considerations.

How Does Computer Vision Improve Quality Control Processes?

How Does Computer Vision Improve Quality Control Processes?

22/01/2026

Learn how computer vision improves quality control by spotting defects, checking labels, and supporting production processes. See how image processing, object detection, neural networks, and OCR help factories boost product quality—and how TechnoLynx can offer tailored solutions for your needs.

GPU‑Powered Machine Learning with NVIDIA cuML

GPU‑Powered Machine Learning with NVIDIA cuML

21/01/2026

Understand how GPU‑powered machine learning with NVIDIA cuML helps teams train models faster, work with larger data sets, and build stronger solutions without heavy infrastructure demands.

CUDA vs ROCm: Choosing for Modern AI

CUDA vs ROCm: Choosing for Modern AI

20/01/2026

A practical comparison of CUDA vs ROCm for GPU compute in modern AI, covering performance, developer experience, software stack maturity, cost savings, and data‑centre deployment.

Best Practices for Training Deep Learning Models

Best Practices for Training Deep Learning Models

19/01/2026

A clear and practical guide to the best practices for training deep learning models, covering data preparation, architecture choices, optimisation, and strategies to prevent overfitting.

Measuring GPU Benchmarks for AI

Measuring GPU Benchmarks for AI

15/01/2026

A practical guide to GPU benchmarks for AI; what to measure, how to run fair tests, and how to turn results into decisions for real‑world projects.

GPU‑Accelerated Computing for Modern Data Science

GPU‑Accelerated Computing for Modern Data Science

14/01/2026

Learn how GPU‑accelerated computing boosts data science workflows, improves training speed, and supports real‑time AI applications with high‑performance parallel processing.

CUDA vs OpenCL: Picking the Right GPU Path

CUDA vs OpenCL: Picking the Right GPU Path

13/01/2026

A clear, practical guide to cuda vs opencl for GPU programming, covering portability, performance, tooling, ecosystem fit, and how to choose for your team and workload.

Performance Engineering for Scalable Deep Learning Systems

Performance Engineering for Scalable Deep Learning Systems

12/01/2026

Learn how performance engineering optimises deep learning frameworks for large-scale distributed AI workloads using advanced compute architectures and state-of-the-art techniques.

Choosing TPUs or GPUs for Modern AI Workloads

Choosing TPUs or GPUs for Modern AI Workloads

10/01/2026

A clear, practical guide to TPU vs GPU for training and inference, covering architecture, energy efficiency, cost, and deployment at large scale across on‑prem and Google Cloud.

Energy-Efficient GPU for Machine Learning

Energy-Efficient GPU for Machine Learning

9/01/2026

Learn how energy-efficient GPUs optimise AI workloads, reduce power consumption, and deliver cost-effective performance for training and inference in deep learning models.