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About Us
Our Work
Computer Vision
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 insights 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.
Custom Models
We design bespoke models to overcome tooling limitations and ensure compatibility with even the most esoteric platforms.
Supervised Design
Need near-perfect reliability or compliance with legal frameworks like the AI Act? We excel at designing human-in-the-loop systems to meet these critical needs.
Video Optimisation
From video streaming to compression, we tackle potential bottlenecks in your pipeline with tools like FFmpeg.
Our Promise to You
Truthfulness and absolute transparency are the core values of our company, and we will only implement projects that we believe will benefit your business. As the most insightful and responsible team you'll ever partner with, we don't just deliver computer vision solutions, we ensure the journey is seamless, engaging, and focused on your business goals.
grounded in honesty and long-term trust
projects that we will truly benefit your business
thoughtful and collaborative journey
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
We provide transparent, fixed-scope quotes following a free technical consultancy. Our estimation process involves:
Your Intellectual Property (IP) and data security are our top priorities. We ensure protection through:
TechnoLynx has years of expertise in traditional CV and Deep Learning-based vision systems. Our portfolio includes:
We utilize the industry-standard AI stack to ensure high performance and maintainability:
Yes, we specialize in resource-constrained AI deployment. We optimize models for:
Production reliability comes from modular, observable pipelines, not from squeezing a few more accuracy points out of a single model. Real-world inputs break the assumptions of off-the-shelf demos: lighting drift, occlusions, edge-case classes, and data quality decay all degrade end-to-end performance even when offline metrics look strong. We design CV systems where each stage (capture, pre-processing, detection, post-processing, escalation) is independently testable and instrumented — see why off-the-shelf CV models fail in production and how to architect a modular CV pipeline.
20/04/2026
Off-the-shelf CV models degrade in production due to variable conditions, class imbalance, and throughput demands that benchmarks never test.
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.
26/04/2026
Custom CV models are justified when the domain is specialised and off-the-shelf accuracy is insufficient. Otherwise, customisation adds waste.
15/05/2025
See how TechnoLynx helped CloudRF speed up signal propagation and tower placement simulations with GPU acceleration, custom algorithms, and cross-platform support. Faster, smarter radio frequency planning made simple.
10/12/2024
Hierarchical SKU classification using DINO embeddings and few-shot learning — above 95% accuracy at ~1k classes, above 83% at ~2k.
20/11/2024
Browser-deployed face quality classifier rebuilt around a single multiclassifier, WebGL pixel capture, and explicit device-capability gating.
20/09/2024
Per-shelf share-of-shelf measurement in area and count modes, with unknown-product handling treated as a first-class operational output.
15/07/2024
In-cart perception for autonomous retail checkout: detection, tracking, adaptive FPS sampling, and a session-scoped cart-state model.
12/03/2024
See how our team applied a case study approach to build a real-time Kazakh text-to-speech solution using ONNX, deep learning, and different optimisation methods.
15/12/2023
Case study on moving a GPU application from OpenCL to Metal for our client V-Nova. Boosts performance, adds support for real-time apps, VR, and machine learning on Apple M1/M2 chips.
15/10/2023
Camera-based barcode pipeline for in-cart capture: YOLO localisation, ensemble decoding, multi-frame polling — 86.7% vs Dynamsoft 80%.
6/06/2023
Case study on using Generative AI for stock market prediction. Combines sentiment analysis, natural language processing, and large language models to identify trading opportunities in real time.
15/05/2023
How TechnoLynx modelled AI inference performance across GPU architectures — delivering two tools (topology-level performance predictor and OpenCL GPU characteriser) plus engineering education that changed how the client's team thinks about GPU cost.
10/02/2023
How TechnoLynx built a cost-efficient multi-target multi-camera tracking system for a smart retail deployment — real-time tracking across non-overlapping CCTV cameras using probabilistic trajectory prediction and consistent global identity.
11/01/2023
How TechnoLynx built a hybrid action recognition system for a smart retail environment — detecting suspicious behaviour in real time using transfer learning and a rules-based approach on cost-effective CCTV.
7/05/2026
Digital shelf monitoring uses CV to detect out-of-stocks, planogram compliance, and pricing errors. What the systems actually detect and where accuracy drops.
Deep learning for image processing in production: CNN vs ViT tradeoffs, training data requirements, augmentation, deployment optimisation, and.
When synthetic faces defeat pretrained detectors: anti-spoofing challenges, liveness detection requirements, and when custom models are unavoidable.
AI CCTV monitoring vs human monitoring: cost comparison, coverage capability, response time tradeoffs, and what AI handles well vs where human judgment is.
CCTV face recognition: resolution requirements, angle and lighting challenges, false positive rates, GDPR compliance, and why production performance lags.
6/05/2026
AI CCTV for building security: intrusion detection, people counting, loitering analytics, camera placement strategy, and storage and bandwidth.
Wired CCTV systems for AI analytics need more than high resolution. Codec support, edge processing, and integration architecture determine analytics quality.
Automated visual inspection in pharma uses computer vision to detect defects in vials, syringes, and tablets — faster and more consistently than human.
Build automated visual inspection systems that work: hardware setup, model selection (classification vs detection vs segmentation), and managing.
Aseptic manufacturing prevents microbial contamination during sterile drug production. AI monitoring addresses the environmental control gaps humans miss.
4K security cameras for AI analytics: bandwidth and storage costs, where higher resolution improves results, compression artifacts and AI accuracy.
5/05/2026
CV in pharmacy retail addresses unique challenges: regulated product tracking, controlled substance security, and planogram compliance across thousands of SKUs.
AI-enhanced visual inspection replaces rule-based defect detection with learned representations — but requires validated training data matching production variability.
Facial recognition accuracy drops 10–40% between controlled enrollment conditions and production CCTV due to angle, lighting, and resolution.
Store analytics CV must distinguish 'detected' from 'measured with business-decision confidence.' Most deployments conflate the two.
Pharma supply chain AI delivers measurable ROI in three areas: serialisation verification, cold-chain anomaly prediction, and visual inspection automation.
CV-based loss prevention must handle thousands of SKUs under variable lighting. Single-model approaches produce unactionable alert volumes at scale.
4/05/2026
IVA shifts surveillance alerting from pixel-change detection to behaviour understanding. But only modular pipeline architectures deliver this in practice.
1/05/2026
Cross-platform TTS to iOS, Android and browser stays consistent only if compression is decided at training time — distill once, export to ONNX.
Generative models trained on normal frames detect rare video anomalies without labelled anomaly data — reconstruction error is the score.
30/04/2026
Operators stop trusting CV alerts when the pipeline is opaque. Observable, modular CCTV pipelines decompose decisions into auditable stages.
Retail CV deployments meet products outside the training catalogue. The architectural choice: silent misclassification or a designed review loop.
29/04/2026
Client-side ML misses latency targets when the device capability baseline is set after architecture selection rather than before. Sequence matters.
Graceful degradation in production SKU recognition is an architectural property: predictable automation rate as the catalogue grows.
28/04/2026
Surveillance false alarms are an architecture problem, not a sensitivity setting. Modular pipelines reduce them; monolithic ones cannot.
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.
25/04/2026
Edge CV trades accuracy for latency and bandwidth savings. Quantisation, model selection, and hardware matching determine whether the trade-off works.
24/04/2026
Retail CV ROI comes from shrinkage reduction, planogram compliance, and checkout automation — not AI dashboards. Measure what changes operationally.
23/04/2026
CV system degradation after deployment is usually a data problem. Annotation inconsistency, domain shift, and data drift are the structural causes.
CV-based pharma QC inspection is a production engineering problem, not a model accuracy problem. It requires data, validation, and pipeline design.
21/04/2026
Machine vision is deterministic and auditable. Computer vision is adaptive and generalisable. The choice depends on defect complexity, not preference.
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.
10/11/2025
Why computer vision systems trained on benchmarks fail on real inputs, and how attention mechanisms, context modelling, and multi-scale features close the gap.
7/11/2025
Neural network visualisation: how activation maps, layer inspection, and feature attribution reveal what a model has learned and where it will fail.
12/05/2025
Multi-object tracking in production: handling occlusion, re-identification, and real-time latency constraints in industrial and retail camera systems.
24/04/2025
Integrating computer vision into assembly lines: inspection system design, detection accuracy targets, and edge deployment considerations for manufacturing environments.
10/04/2025
Video pipeline optimisation: how encoding, transmission, and decoding decisions determine real-time computer vision latency and processing throughput at scale.
27/03/2025
Human-in-the-loop AI: how to design review queues that maintain throughput while keeping humans in control of low-confidence and edge-case decisions.
24/03/2025
Quality control with computer vision: inspection pipeline design, defect detection architectures, and the measurement factors that determine false-reject rates in production.
17/03/2025
Computer vision for inventory counting and tracking: how shelf-state monitoring, object detection, and anomaly detection reduce manual audit overhead in warehouses and retail.
Explainability in computer vision: how saliency maps, attention visualisation, and interpretable architectures make CV models auditable and correctable in production.
10/02/2025
Real-time face detection in production: CNN architecture choices, detection pipeline design, and the latency constraints that determine deployment feasibility.
19/11/2024
Learn how AI aids in sorting and counting with applications in various industries. Get hands-on with code examples for sorting and counting apples based on size and ripeness using instance segmentation and YOLO-World object detection.
