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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 insights 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.
5/05/2026
CV in pharmacy retail addresses unique challenges: regulated product tracking, controlled substance security, and planogram compliance across thousands of SKUs.
Talent intelligence uses ML to map skills, predict attrition, and identify internal mobility — but only with sufficient longitudinal employee data.
Inference infrastructure decisions should be driven by measured performance under your actual workload — vendor benchmarks rarely match production conditions.
AI-enhanced visual inspection replaces rule-based defect detection with learned representations — but requires validated training data matching production variability.
New AI-driven monitoring systems detect contamination risk in aseptic filling by analysing environmental and process data continuously rather than via batch sampling.
Facial recognition accuracy drops 10–40% between controlled enrollment conditions and production CCTV due to angle, lighting, and resolution.
CUDA compute capability determines which tensor core operations and precision formats a GPU supports — not just whether CUDA runs.
4/05/2026
TOPS measures theoretical throughput at one precision. It ignores memory bandwidth, software overhead, and workload fit — making it a poor performance predictor.
No single AI agent excels at all task types. The best choice depends on whether your workflow is structured or unstructured.
A100 rental pricing varies 2–5× between providers depending on commitment length, region, and availability. Here is what the market looks like.
MLOps tooling is consolidating around integrated platforms. The operational complexity shifts from integration to configuration and governance.
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.
