Computer Vision

Truthfulness and absolute
transparency.

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

Why Choose Us?

Tailored solutions,
not one-size-fits-all.

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

Classical Vision

Classical Vision

Computer Vision

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

Explainability

Explainability

Computer Vision

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

Cross-Disciplinary

Cross-Disciplinary

Computer Vision

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

Scalable Solutions

Scalable Solutions

Computer Vision

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

Frictionless Onboarding

Frictionless Onboarding

Computer Vision

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

Multi-GPU Optimisation

Computer Vision

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

Custom Models

Custom Models

Computer Vision

We design bespoke models to overcome tooling limitations and ensure compatibility with even the most esoteric platforms.

Supervised Design

Supervised Design

Computer Vision

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.

Cross-Disciplinary

Video Optimisation

Computer Vision

From video streaming to compression, we tackle potential bottlenecks in your pipeline with tools like FFmpeg.

Area of Expertise

Object Detection & Recognition
Object Tracking
Image Classification
Image Segmentation
Anomaly Detection
Face Recognition
Video Analytics
Point Cloud
Performance Optimisation
Quantisation
Pruning
CoreML Conversion
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Our Promise to You

Vision with Integrity,
Impact with Insight

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.

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grounded in honesty and long-term trust

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projects that we will truly benefit your business

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thoughtful and collaborative journey

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Technology Stack

PyTorch
TorchScript
TensorFlow
LiteRT
TensorRT
Face Recognition
ONNX
OpenCV
YOLO
Python
NumPy
SciPy
Numba
C
C++
CUDA

Client Testimonials

Frequently Asked Questions

How does TechnoLynx provide project cost estimates?

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We provide transparent, fixed-scope quotes following a free technical consultancy. Our estimation process involves:

  • Consultation: Assessing scope, technical complexity, and hardware requirements.
  • Feasibility Study: Estimating development time and resource allocation.
  • Detailed Breakdown: You receive a formal quote outlining specific timelines, milestones, and deliverables.

How does TechnoLynx protect IP and confidentiality?

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Your Intellectual Property (IP) and data security are our top priorities. We ensure protection through:

  • Legal Safeguards: Signing NDAs before project disclosure and adhering to EU data protection laws.
  • Full IP Ownership: Contractual guarantees that all deliverables belong to you.
  • Domain Exclusivity: To avoid conflicts of interest, we commit to only one client per specific technology niche.

What is TechnoLynx’s experience in Computer Vision?

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TechnoLynx has years of expertise in traditional CV and Deep Learning-based vision systems. Our portfolio includes:

  • Object Detection, Tracking, and Recognition.
  • Semantic and Instance Segmentation.
  • Production-grade optimization for high-accuracy, real-world deployments.

Which Deep Learning frameworks and libraries do you use?

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We utilize the industry-standard AI stack to ensure high performance and maintainability:

  • Core Frameworks: PyTorch, TensorFlow, and OpenCV.
  • Data Science: NumPy, Pandas, SciPy, and Scikit-learn.
  • Deployment: ONNX, TensorRT, and CoreML.

Can TechnoLynx deploy AI models to Edge and Mobile devices?

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Yes, we specialize in resource-constrained AI deployment. We optimize models for:

  • Edge Hardware: Using TensorRT and ONNX for high-throughput inference on NVIDIA Jetson and similar platforms.
  • Mobile (iOS/Android): Leveraging CoreML and specialized quantization for seamless mobile integration.

What does production computer vision require beyond model accuracy?

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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.

Featured Insights

Case Studies

Case Study: CloudRF  Signal Propagation and Tower Optimisation

Case Study: CloudRF  Signal Propagation and Tower Optimisation

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.

Case Study: Large-Scale SKU Product Recognition

Case Study: Large-Scale SKU Product Recognition

10/12/2024

Hierarchical SKU classification using DINO embeddings and few-shot learning — above 95% accuracy at ~1k classes, above 83% at ~2k.

Case Study: WebSDK Client-Side ML Inference Optimisation

Case Study: WebSDK Client-Side ML Inference Optimisation

20/11/2024

Browser-deployed face quality classifier rebuilt around a single multiclassifier, WebGL pixel capture, and explicit device-capability gating.

Case Study: Share-of-Shelf Analytics

Case Study: Share-of-Shelf Analytics

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.

Case Study: Smart Cart Object Detection and Tracking

Case Study: Smart Cart Object Detection and Tracking

15/07/2024

In-cart perception for autonomous retail checkout: detection, tracking, adaptive FPS sampling, and a session-scoped cart-state model.

Case-Study: Text-to-Speech Inference Optimisation on Edge (Under NDA)

Case-Study: Text-to-Speech Inference Optimisation on Edge (Under NDA)

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.

Case-Study: V-Nova - GPU Porting from OpenCL to Metal

Case-Study: V-Nova - GPU Porting from OpenCL to Metal

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.

Case Study: Barcode Detection for Autonomous Retail

Case Study: Barcode Detection for Autonomous Retail

15/10/2023

Camera-based barcode pipeline for in-cart capture: YOLO localisation, ensemble decoding, multi-frame polling — 86.7% vs Dynamsoft 80%.

Case-Study: Generative AI for Stock Market Prediction

Case-Study: Generative AI for Stock Market Prediction

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.

Case-Study: Performance Modelling of AI Inference on GPUs

Case-Study: Performance Modelling of AI Inference on GPUs

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.

Case Study: Multi-Target Multi-Camera Tracking

Case Study: Multi-Target Multi-Camera Tracking

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.

Case-Study: Action Recognition for Security (Under NDA)

Case-Study: Action Recognition for Security (Under NDA)

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.

Case-Study: V-Nova - Metal-Based Pixel Processing for Video Decoder

Consulting: AI for Personal Training Case Study - Kineon

Case-Study: A Generative Approach to Anomaly Detection (Under NDA)

Case Study: Accelerating Cryptocurrency Mining (Under NDA)

Case Study - AI-Generated Dental Simulation

Case Study - Fraud Detector Audit (Under NDA)

Case Study - Embedded Video Coding on GPU (Under NDA)

Case Study - Accelerating Physics -Simulation Using GPUs (Under NDA)

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