Generative & Agentic AI

Share knowledge, spark ideas,
refine solutions.

Get Expert Input Reach out
arrow icon
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.

Custom Models

Leaders in Gen AI

Generative AI

We’ve been mastering generative AI since 2019, with a deep understanding of latent spaces, embeddings, and LLMs.

Supervised Design

Model Optimisation for Inference

Generative AI

Our expertise in optimising large model inference ensures faster, more efficient deployments.

Cross-Disciplinary

Explainable and Verifiable

Generative AI

We prioritise transparency with techniques like RAGs, making your AI solutions explainable and verifiable.

Scalable Solutions

Multi-GPU Optimisation

Generative AI

We fine-tune large models using TensorRT to maximise multi-GPU performance and efficiency.

Frictionless Onboarding

Ethical and Trustworthy

Generative AI

We ensure compliance with regulations while mitigating bias to create fair and ethical AI systems.

Multi-GPU Optimisation

Reduced Onboarding Costs

Generative AI

Our use of self-supervised techniques minimises onboarding costs and streamlines adoption.

Multi-GPU Optimisation

Intelligent Automation

Generative AI

We design agentic AI workflows, automating tasks and empowering dynamic, adaptive systems.

Multi-GPU Optimisation

Scalable Custom Solutions

Generative AI

Our company is proud to offer solutions that are designed for optimal scalability, ranging from data management to computational performance.

Multi-GPU Optimisation

Advanced Simulation

Generative AI

Our capabilities in simulation and prototyping accelerate testing and bring your ideas to life faster.

Area of Expertise

Automation with Agents
Relevancy Enhancement with RAG
Hyper-Personalisation
LLM Context Management
LLM Content Localisation
Physics-Based Simulation
Hybrid Search with FMs and RAG
Data Augmentation
Prompt Engineering
Fine-Tuning
Distillation
Quantisation
Team image

Built Together

Collaboration That Powers Innovation

We are a team that brings unique expertise in generative and agentic AI, making every step of the process enjoyable and collaborative. We don't just build powerful AI systems — we share our knowledge and refine solutions with you. Communication is at the core of our approach, and we are constantly seeking to optimise our processes to deliver results!

arrow icon

expertise in Generative AI with a creative mindset

arrow icon

open knowledge-sharing every step of the way

arrow icon

continuous communication, real outcomes

Meet the Team Let's see
arrow icon

Technology Stack

PyTorch Lightning
TorchScript
TensorFlow
LiteRT
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
Python
C
C++
R

Client Testimonials

Frequently Asked Questions

Should I train Generative AI from scratch or use pre-trained models?

+

The choice depends on your specific balance of novelty, cost, and data privacy. TechnoLynx helps you navigate this decision:

  • Pre-trained Models (Fine-Tuning): Best for speed-to-market and cost efficiency when leveraging existing knowledge bases.
  • Training from Scratch: Essential when you require absolute novelty, domain-specific architecture, or strict data sovereignty.

Is limited data a blocker for Generative AI projects?

+

No, limited data is rarely a blocker. TechnoLynx employs advanced techniques to overcome data scarcity and build robust models, including:

  • Data Augmentation & Synthesis: Generating synthetic data to expand your dataset.
  • Transfer Learning: Leveraging knowledge from related tasks.
  • Few-Shot Learning: Training models to recognize patterns with minimal examples.

How does TechnoLynx design scalable Generative AI applications?

+

We build scalability into the architecture from day one using a hybrid approach:

  • Hybrid Compute: Balancing Edge and Cloud processing to optimize latency and cost.
  • Modular Design: Using reusable components to allow flexible model swapping.
  • Automated Pipelines: Implementing active checkpoints and automated data curation to ensure the system grows with your user base.

What data types does TechnoLynx handle for AI projects?

+

TechnoLynx specializes in multimodal Generative AI, handling diverse data types including:

  • Text (NLP): For Large Language Models (LLMs) and chatbots.
  • Computer Vision: Images and Video for generation, tracking, and object recognition.
  • Audio: Speech recognition and synthesis.
  • Structured Data: Tabular and time-series data for predictive analytics.

Is generative AI only about large language models?

+

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.

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)

Related Posts

Our blogs See all
arrow icon