Monetise the 5G Edge

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Globe

Industry Landscape

The future of telecommunications

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

Unprecedented data

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.

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Signal

Why Choose Us?

Our Promise

Enterprises expect measurable outcomes. Our modules deliver human‑perceived quality metrics and low‑latency pipelines, accelerating pilots into offer‑ised services operators can market.

Classical Vision

Perceptual QoE

Telecommunication

Fuse network telemetry with perceptual quality models for video and gaming, prioritising actions that move SLAs and NPS.

Explainability

Edge CV

Telecommunication

Portable computer‑vision kernels and adaptive streaming meet tough latency and bandwidth targets for industrial video and XR.

Cross-Disciplinary

Governed Scale

Telecommunication

Consistent policy and rollout automation across heterogeneous edge nodes allow operators to scale services safely.

Areas of Expertise

Perceptual QoE analytics for video/gaming
Edge computer vision services
XR adaptive streaming at MEC
Speech-to-text for critical comms
Multi-tenant edge governance
Phone & Laptop

Partner Proposition

Experience platforms, portable modules

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.

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Our technological capabilities
are centred around three core pillars:

Computer Vision Services

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.

Generative AI

We are leaders in generative AI. We provide faster deployments with optimised inference. Our systems focus on ethical AI and reducing bias.

We also offer intelligent automation for flexible workflows. Additionally, we have advanced simulation and prototyping tools.

GPU Performance Engineering

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.

Technology Stack

PyTorch
TorchScript
TensorFlow
LiteRT
TensorRT
Face Recognition
ONNX
OpenCV
YOLO
Python
NumPy
SciPy
Numba
C
C++
CUDA
Unity
Unreal Engine
OpenXR
ARKit
ARCore
Vuforia
DeepAR
A Frame
WebXR
OpenCL
Vulkan
DirectX 12
Metal
WebGL
WebGPU
SteamVR SDK
Oculus SDK
Wave SDK
CloudXR
NVIDIA Omniverse
NVIDIA PhysX
PyTorch Lighting
TF-GAN
LangChain
LangGraph
LangSmith
LlamaIndex
W&B Weave
Hugging Face Transformers
LibFewShot
PandaAI
RagFlow
GraphRAG
JAX
Solo-learn
VFormer
Vertex AI Agent Builder
Vertex AI Search
AWS Bedrock
NVIDIA AI Foundry
NVIDIA NeMO
R
2019
Founded in
95%+
Client Satisfaction Rate
20+
Successful Projects Delivered

Client Testimonials

Advanced Edge & Perceptual AI FAQ

How does your QoE monitoring work without accessing content?

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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.

Can your Speech-to-Text service operate in disconnected environments?

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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.

  • Optimized Inference: We prune and quantize models to run locally on-device.
  • Resilience: Operates at full accuracy in remote or secured "air-gapped" environments.
  • Cost Efficiency: Eliminates the recurring API costs and latency of cloud-based STT providers.

How do you manage multi-vendor hardware at the edge?

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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.

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: 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: 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

Learn how TechnoLynx helps reduce inference costs for trained neural networks and real-time applications including natural language processing, video games, and large language models.

Case Study: Multi-Target Multi-Camera Tracking

Case Study: Multi-Target Multi-Camera Tracking

10/02/2023

Learn how TechnoLynx built a cost-efficient, AI-powered multi-target tracking system using existing CCTV infrastructure. Real-time object tracking across non-overlapping cameras using global and local IDs.

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

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

11/01/2023

See how TechnoLynx used AI-powered action recognition to improve video analysis and automate complex tasks. Learn how smart solutions can boost efficiency and accuracy in real-world applications.

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

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

15/12/2022

TechnoLynx improved V-Nova’s video decoder with GPU-based pixel processing, Metal shaders, and efficient image handling for high-quality colour images across Apple devices.

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

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

15/12/2022

TechnoLynx improved V-Nova’s video decoder with GPU-based pixel processing, Metal shaders, and efficient image handling for high-quality colour images across Apple devices.

Consulting: AI for Personal Training Case Study - Kineon

Consulting: AI for Personal Training Case Study - Kineon

2/11/2022

TechnoLynx partnered with Kineon to design an AI-powered personal training concept, combining biosensors, machine learning, and personalised workouts to support fitness goals and personal training certification paths.

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

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

22/05/2022

See how we successfully compeleted this project using Anomaly Detection!

Case Study: Accelerating Cryptocurrency Mining (Under NDA)

Case Study: Accelerating Cryptocurrency Mining (Under NDA)

29/12/2020

Our client had a vision to analyse and engage with the most disruptive ideas in the crypto-currency domain. Read more to see our solution for this mission!

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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