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About Us
Our Work
We design and optimise advanced computer vision, AI, and GPU‑accelerated solutions, turning complex ideas into scalable, high‑performance systems for real‑world impact.
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.
Visual Computing for Life Sciences
Privacy‑First Surveillance AI
Monetise the 5G Edge
AI-Powered Retail Innovation
Accelerating Connectivity
We're not just your tech team — we're your thought partner. Every collaboration begins with deep understanding, followed by sharp execution.
We offer expertise in foundational computer vision techniques to deliver versatile and performance-optimised solutions.
Transparency matters. Our solutions prioritise explainability, catering to markets with stringent legal and ethical requirements.
Our peripheral knowledge across various fields enhances your projects with unique, cross-disciplinary insight s for innovative solutions.
We craft solutions with scalability in mind, combining optimisation, adaptability, and multi-GPU support for robust performance.
We specialise in designing systems that streamline onboarding processes, thereby reducing costs and minimising time-to-adoption for your teams and workflows.
Reduce cloud processing expenses with our expertise in multi-GPU optimisation, designed to handle demanding workloads efficiently.
GPU Performance Engineering
Computer Vision
Generative AI
Who We Are
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.
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
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
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
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
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
Feasibility comes before scope. We assess data, evaluation method, integration cost, and operational constraints up front and refuse engagements that depend on super-human-level performance to deliver value. See how to evaluate GenAI feasibility before you build and why most enterprise AI projects fail.
You do. We work in outcome-owned engagements: every deliverable and the underlying IP belong to the client. We sign NDAs first, work with one client per technology niche to avoid conflicts of interest, and structure milestones so each one produces a packageable, transferable artifact rather than only a future promise.
Yes. Validation pathways under CSA, CSV, GAMP 5 second edition and Annex 11 already accommodate well-scoped AI/ML systems, and the regulatory perimeter is often narrower than internal teams assume. See why pharma delay costs more than adoption and our life sciences practice.
It depends on what you need. A Technical Business Analysis or feasibility assessment usually takes a few weeks; an R&D Sprint or proof of concept is typically a few weeks to a couple of months; a full development engagement runs over several months. We scope each phase explicitly so you know what is committed before work begins.
Yes. We sign mutual NDAs before exchanging confidential material, and we apply tight IP clauses with both our clients and our own employees so anything generated within a project is owned by the client. For regulated work we operate under CSA, CSV, GAMP 5 and Annex 11 frameworks, and for personal data we apply GDPR-compliant pipelines including data minimisation, de-identification and human-in-the-loop review where appropriate.
Engagements are scoped to your problem, not sold off a price list. A short feasibility assessment is a low-cost entry point that de-risks larger commitments; sprints and full developments are quoted against a written scope and milestone plan. Talk to us with a one-paragraph problem description and we will reply with an indicative range.
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.
28/04/2026
CV models that pass accuracy tests at 500 SKUs fail in production above 1,000 — not from one cause but from four simultaneous failure axes.
20/04/2026
Most GenAI use cases fail at feasibility, not implementation. Assess data, accuracy tolerance, and integration complexity before building.
Pharma AI adoption stalls from regulatory misperception, scope inflation, and transformation assumptions. Each delay has a measurable manufacturing cost.
23/04/2026
Evaluate AI consultancies on technical depth, delivery evidence, and knowledge transfer — not on slide decks, partnership badges, or client logo walls.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
