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.

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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.

Technical Business Analysis & Consulting → R&D Outsourcing → Custom Software Development → MLOps →

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.

Technologies

GPU Performance Engineering

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Computer Vision

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Generative AI

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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.

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Client Testimonials

TechnoLynx delivered the project on time and provided quality outputs that met the client's expectations. The team was proactive in providing ideas and suggestions, and they were careful at properly planning the tasks. The client also praised the team's expertise in GPU programming and AI.

Guido Meardi - CEO

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Check V-Nova

TechnoLynx's skill in low-level software development was impressive. TechnoLynx was able to create four prototypes with common components and an interface for easy maintenance. The client was extremely happy with the solution's speed. Moreover, their communication was seamless and straightforward.

Alex Farrant - Director

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Check CloudRF

TechnoLynx's unique aspect is that they're able to transform complex theories into practicable and applicable results. TechnoLynx provides research reports and architecture planning documents. The team is able to transform complex theories into practicable and applicable results. TechnoLynx's project management is strong and delivers work on time without hardware issues, being responsive through virtual meetings.

Forrest Smith - CEO & Co-Founder

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Check Kineon

I’m delighted with our collaboration with their team. Thanks to TechnoLynx's work, the client has been able to co-author two patents. They lead responsive project management to solve problems quickly. The team also praises their skilled and knowledgeable team.

Gil Hagi - CEO

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Check Tasty

We had high-efficiency meetings. TechnoLynx’s work resulted in a successful breakthrough, and their input improved the client’s app. Their flexible and organised project management cultivated a healthy collaboration experience. Ultimately, their professionalism and commitment were impressive.

Anonymous - CEO

Articles

Cost, Efficiency, and Value Are Not the Same Metric

Cost, Efficiency, and Value Are Not the Same Metric

17/04/2026

Performance per dollar. Tokens per watt. Cost per request. These sound like the same thing said differently, but they measure genuinely different dimensions of AI infrastructure economics. Conflating them leads to infrastructure decisions that optimize for the wrong objective.
Precision Is an Economic Lever in Inference Systems

Precision Is an Economic Lever in Inference Systems

17/04/2026

Precision isn't just a numerical setting — it's an economic one. Choosing FP8 over BF16, or INT8 over FP16, changes throughput, latency, memory footprint, and power draw simultaneously. For inference at scale, these changes compound into significant cost differences.
Precision Choices Are Constrained by Hardware Architecture

Precision Choices Are Constrained by Hardware Architecture

17/04/2026

You can't run FP8 inference on hardware that doesn't have FP8 tensor cores. Precision format decisions are conditional on the accelerator's architecture — its tensor core generation, native format support, and the efficiency penalties for unsupported formats.
Steady-State Performance, Cost, and Capacity Planning

Steady-State Performance, Cost, and Capacity Planning

17/04/2026

Capacity planning built on peak performance numbers over-provisions or under-delivers. Real infrastructure sizing requires steady-state throughput — the predictable, sustained output the system actually delivers over hours and days, not the number it hit in the first five minutes.
How Benchmark Context Gets Lost in Procurement

How Benchmark Context Gets Lost in Procurement

16/04/2026

A benchmark result starts with full context — workload, software stack, measurement conditions. By the time it reaches a procurement deck, all that context is gone. The failure mode is not wrong benchmarks but context loss during propagation.
Building an Audit Trail: Benchmarks as Evidence for Governance and Risk

Building an Audit Trail: Benchmarks as Evidence for Governance and Risk

16/04/2026

High-value AI hardware decisions need traceable evidence, not slide-deck bullet points. When benchmarks are documented with methodology, assumptions, and limitations, they become auditable institutional evidence — defensible under scrutiny and revisitable when conditions change.
The Comparability Protocol: Why Benchmark Methodology Defines What You Can Compare

The Comparability Protocol: Why Benchmark Methodology Defines What You Can Compare

16/04/2026

Two benchmark scores can only be compared if they share a declared methodology — the same workload, precision, measurement protocol, and reporting conditions. Without that contract, the comparison is arithmetic on numbers of unknown provenance.
A Decision Framework for Choosing AI Hardware

A Decision Framework for Choosing AI Hardware

16/04/2026

Hardware selection is a multivariate decision under uncertainty — not a score comparison. This framework walks through the steps: defining the decision, matching evaluation to deployment, measuring what predicts production, preserving tradeoffs, and building a repeatable process.
How Benchmarks Shape Organizations Before Anyone Reads the Score

How Benchmarks Shape Organizations Before Anyone Reads the Score

16/04/2026

Before a benchmark score informs a purchase, it has already shaped what gets optimized, what gets reported, and what the organization considers important. Benchmarks function as decision infrastructure — and that influence deserves more scrutiny than the number itself.
Accuracy Loss from Lower Precision Is Task‑Dependent

Accuracy Loss from Lower Precision Is Task‑Dependent

16/04/2026

Reduced precision does not produce a uniform accuracy penalty. Sensitivity depends on the task, the metric, and the evaluation setup — and accuracy impact cannot be assumed without measurement.
Precision Is a Design Parameter, Not a Quality Compromise

Precision Is a Design Parameter, Not a Quality Compromise

16/04/2026

Numerical precision is an explicit design parameter in AI systems, not a moral downgrade in quality. This article reframes precision as a representation choice with intentional trade-offs, not a concession made reluctantly.
Mixed Precision Works by Exploiting Numerical Tolerance

Mixed Precision Works by Exploiting Numerical Tolerance

16/04/2026

Not every multiplication deserves 32 bits. Mixed precision works because neural network computations have uneven numerical sensitivity — some operations tolerate aggressive precision reduction, others don't — and the performance gains come from telling them apart.
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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.
Choosing Vulkan, OpenCL, SYCL or CUDA for GPU Compute

Choosing Vulkan, OpenCL, SYCL or CUDA for GPU Compute

28/01/2026

A practical comparison of Vulkan, OpenCL, SYCL and CUDA, covering portability, performance, tooling, and how to pick the right path for GPU compute across different hardware vendors.
Deep Learning Models for Accurate Object Size Classification

Deep Learning Models for Accurate Object Size Classification

27/01/2026

A clear and practical guide to deep learning models for object size classification, covering feature extraction, model architectures, detection pipelines, and real‑world considerations.
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.