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Industries
About Us
Our Work
Computer Vision
Why Choose Us?
Off-the-shelf vision models look strong in a notebook and fail on real inputs. We design the pipeline around the failure modes that actually bite: lighting drift, occlusion, edge-case classes, and data decay.
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
We only take on vision work we believe will pay off, and we will tell you when the model is not the real problem. In production, the hard part is rarely the network: it is the data pipeline, the edge cases, and the deployment target. We scope around those first, then build the system that holds up once real inputs arrive.
we say so when the model is not the bottleneck
pipelines that stay observable and testable in production
one client per niche, so your edge stays yours
Where This Goes Next
Computer vision is where many of our engagements start, but the next step depends on what you need. If a model already runs and the problem is cost or latency, that is the Inference Cost-Cut Pack. If it gives wrong answers in production with no release gates catching the regressions, that is the Production AI Monitoring Harness. If the bottleneck is the GPU and inference layer underneath, we engineer that too.
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 a transparent, scoped quote following a free technical consultation. 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 domain conditions diverge from training distributions and off-the-shelf accuracy is insufficient.
15/05/2025
See how TechnoLynx helped CloudRF speed up signal propagation and tower placement simulations with GPU acceleration, custom algorithms, and…
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…
15/12/2023
Case study on moving a GPU application from OpenCL to Metal for our client V-Nova.
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…
15/05/2023
How TechnoLynx modelled AI inference performance across GPU architectures — delivering two tools (topology-level performance predictor and OpenCL GPU…
10/02/2023
How TechnoLynx built a cost-efficient multi-target multi-camera tracking system for a smart retail deployment
11/01/2023
How TechnoLynx built a hybrid action recognition system for a smart retail environment
