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Industries
About Us
Our Work
Industry Landscape
The future of telecommunications is intelligent. TechnoLynx offers a high-quality AI and video analytics platform. It goes beyond network KPIs to improve user experience. The platform ensures smooth critical communications and helps create new revenue streams at the 5G edge.
The problem
Telecom operators face a dual challenge: managing unprecedented data volumes across complex networks while striving to deliver a flawless user experience. Within the telecommunications industry, traditional network metrics often fail to capture perceived quality, leading to customer churn.
Meanwhile, the need for ultra-reliable critical communications is non-negotiable, and the race to monetise investments in 5G and edge computing requires new, scalable enterprise solutions that this industry is currently not equipped to offer on its own.
Why Choose Us?
Enterprises expect measurable outcomes. Our modules deliver human‑perceived quality metrics and low‑latency pipelines, accelerating pilots into offer‑ised services operators can market.
Perceptual QoE
Telecommunication
Fuse network telemetry with perceptual quality models for video and gaming, prioritising actions that move SLAs and NPS.
Edge CV
Portable computer‑vision kernels and adaptive streaming meet tough latency and bandwidth targets for industrial video and XR.
Governed Scale
Consistent policy and rollout automation across heterogeneous edge nodes allow operators to scale services safely.
Partner Proposition
We turn networks into experience platforms: perceptual QoE, edge CV, and XR pipelines that hit latency/energy targets. Portable modules fuse telemetry with AI, improving SLAs and reducing churn—ready to productise and price.
Transform your processes with advanced visual recognition and analysis. Our services include skills in classical computer vision. We design systems with human supervision for legal compliance.
We optimise video pipelines using tools like FFmpeg. We create custom models that can adapt. We also provide explainable AI for ethical transparency.
We are leaders in generative and agentic AI. We provide faster deployments with optimised inference and bias-mitigated, ethical AI.
We also build autonomous agent workflows that plan, call tools and complete multi-step tasks — plus advanced simulation and prototyping.
We deliver immersive XR solutions with cross-platform development (Unity 6), GPU performance optimisation, and expertise in NVIDIA Omniverse and CloudXR. We also use reinforcement learning for intelligent XR environments.
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 analyze the "DNA" of the stream, not the pixels. Our perceptual Quality of Experience (QoE) solution uses heuristic-based pattern matching on stream metadata (e.g., arrival jitter, packet loss patterns, and bitrate fluctuations).
By leveraging our expertise in video pipeline optimization, we can accurately predict human-perceived quality degradation (like buffering or macroblocking) without ever decrypting or processing the visual payload, ensuring 100% data privacy.
Yes. We build for "Network-Optional" reliability. Our Speech-to-Text (STT) engine is an edge-native Generative AI model optimized for low-bandwidth and offline scenarios.
We provide a unified Abstraction Layer for heterogeneous hardware. Our edge platform acts as a "middleware" that standardizes the deployment environment across diverse silicon (e.g., NVIDIA, Intel, ARM).
This hardware-agnostic approach allows you to manage consistent AI governance and model updates across your entire fleet of edge nodes, regardless of the underlying vendor or chip architecture.
28/04/2026
Distillation and quantisation both shrink models for edge inference, but for three-or-more platforms only distillation keeps quality consistent.
29/04/2026
Client-side ML misses latency targets when the device capability baseline is set after architecture selection rather than before. Sequence matters.
25/04/2026
Edge CV trades accuracy for latency and bandwidth savings. Quantisation, model selection, and hardware matching decide whether the trade-off works.
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.
