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About Us
Our Work
Generative & Agentic AI
Why Choose Us?
Diffusion, GANs, VAEs, agents, and LLMs each solve a different deployment-constrained problem. We pick the family that fits your data, latency, and compute budget, not the one that is currently fashionable.
Leaders in Gen AI
We’ve been mastering generative AI since 2019, with a deep understanding of latent spaces, embeddings, and LLMs.
Model Optimisation for Inference
Our expertise in optimising large model inference ensures faster, more efficient deployments.
Explainable and Verifiable
We prioritise transparency with techniques like RAGs, making your AI solutions explainable and verifiable.
Multi-GPU Optimisation
We fine-tune large models using TensorRT to maximise multi-GPU performance and efficiency.
Ethical and Trustworthy
We ensure compliance with regulations while mitigating bias to create fair and ethical AI systems.
Reduced Onboarding Costs
Our use of self-supervised techniques minimises onboarding costs and streamlines adoption.
Intelligent Automation
We design agentic AI workflows, automating tasks and empowering dynamic, adaptive systems.
Scalable Custom Solutions
Our company is proud to offer solutions that are designed for optimal scalability, ranging from data management to computational performance.
Advanced Simulation
Our capabilities in simulation and prototyping accelerate testing and bring your ideas to life faster.
Built Together
The best generative AI work happens in the open, with your engineers in the room. We share what we are doing and why, we challenge the scope when the brief points the wrong way, and we leave your team able to run and extend what we built. By the end you should understand the system as well as we do.
we challenge the scope when the brief points the wrong way
your engineers in the room, not handed a black box
you can run and extend what we built after handover
Where This Goes Next
Generative AI is where many engagements begin, but the next step depends on the question. If a committee needs evidence to choose between LLMs, that is the LLM Selection Pack. If a model already runs and the problem is cost or latency, that is the Inference Cost-Cut Pack. Multimodal work often reaches into computer vision and the GPU inference layer underneath.
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
The choice depends on your specific balance of novelty, cost, and data privacy. TechnoLynx helps you navigate this decision:
No, limited data is rarely a blocker. TechnoLynx employs advanced techniques to overcome data scarcity and build robust models, including:
We build scalability into the architecture from day one using a hybrid approach:
TechnoLynx specializes in multimodal Generative AI, handling diverse data types including:
No, LLMs are one family inside a much broader generative landscape. Diffusion models, GANs, VAEs, and audio/video/3D generators all solve different deployment-constrained problems, and the right architecture depends on data, latency, and compute budget rather than on which family is currently fashionable. Picking the wrong family is a common cause of feasibility failure. See generative AI beyond LLMs and how to evaluate GenAI feasibility before you build.
20/04/2026
A four-dimension decision framework for assessing GenAI use case feasibility before development: data, accuracy tolerance, integration, and simpler…
27/04/2026
Moving a GenAI prototype to production means data-pipeline reliability, serving latency, drift and hallucination monitoring — not shipping the notebook.
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
