Machine learning consulting

At TechnoLynx, we're dedicated to helping businesses take advantage of the immense potential of machine learning. Read more about activities.

Machine learning consulting
Written by TechnoLynx Published on 08 Nov 2023

At TechnoLynx, we’re dedicated to helping businesses take advantage of the immense potential of machine learning. Our expertise as machine learning consultants is your gateway to data-driven success.

  • Data-Driven Decisions: In today’s dynamic business landscape, data is the key to informed decision-making. Our machine learning solutions empower you to extract valuable insights, make predictions, and optimize strategies based on data-driven evidence.

  • Tailored Solutions: We understand that every business is unique. Our machine learning consulting services are not one-size-fits-all. We craft custom solutions that align with your specific goals, whether it’s enhancing customer experiences, automating processes, or uncovering hidden opportunities.

  • Security and Compliance: Data security is non-negotiable. We prioritize the highest standards of security and compliance in every solution we deliver, ensuring that your data remains protected while you enjoy the benefits of machine learning.

  • Staying Ahead of the Curve: In the fast-evolving realm of machine learning, staying up-to-date is essential. We’re committed to keeping our clients at the forefront of the latest developments, ensuring your business leverages the most cutting-edge technologies.

  • A Trusted Partner: When you partner with us, you’re not just a client – you’re a valued collaborator. We work alongside you every step of the way, providing guidance, support, and expertise to help you achieve your goals.

Machine learning isn’t just a technology; it’s a strategic advantage. Reach out to us today and start gaining the power of machine learning.

Engineering Task vs Research Question: Why the Distinction Determines AI Project Success

Engineering Task vs Research Question: Why the Distinction Determines AI Project Success

27/04/2026

Engineering tasks have known solutions and predictable timelines. Research questions have uncertain outcomes. Conflating the two causes project failure.
MLOps for Organisations That Have Never Operationalised a Model

MLOps for Organisations That Have Never Operationalised a Model

27/04/2026

MLOps keeps AI models working after deployment. Start with monitoring, versioning, and retraining pipelines — not full platform adoption.
Internal AI Team vs AI Consultants: A Decision Framework for Build or Hire

Internal AI Team vs AI Consultants: A Decision Framework for Build or Hire

26/04/2026

Build internal teams for sustained advantage. Hire consultants for speed, specialisation, and knowledge transfer. Most organisations need both.
How to Assess Enterprise AI Readiness — and What to Do When You Are Not Ready

How to Assess Enterprise AI Readiness — and What to Do When You Are Not Ready

26/04/2026

AI readiness is about data infrastructure, organisational capability, and governance maturity — not technology. Assess all three before committing.
When to Build a Custom Computer Vision Model vs Use an Off-the-Shelf Solution

When to Build a Custom Computer Vision Model vs Use an Off-the-Shelf Solution

26/04/2026

Custom CV models are justified when the domain is specialised and off-the-shelf accuracy is insufficient. Otherwise, customisation adds waste.
How a Structured AI Consulting Engagement Works

How a Structured AI Consulting Engagement Works

25/04/2026

A structured AI engagement moves through assessment, POC, production build, and handoff — with decision gates, not open-ended retainers.
How Multi-Agent Systems Coordinate — and Where They Break

How Multi-Agent Systems Coordinate — and Where They Break

25/04/2026

Multi-agent AI decomposes tasks across specialised agents. Conflicting plans, hallucinated handoffs, and unbounded loops are the production risks.
What an AI POC Should Actually Prove — and the Four Sections Every POC Report Needs

What an AI POC Should Actually Prove — and the Four Sections Every POC Report Needs

24/04/2026

An AI POC should prove feasibility, not capability. It needs four sections: structure, success criteria, ROI measurement, and packageable value.
How to Optimise AI Inference Latency on GPU Infrastructure

How to Optimise AI Inference Latency on GPU Infrastructure

24/04/2026

Inference latency optimisation targets model compilation, batching, and memory management — not hardware speed. TensorRT and quantisation are key levers.
What to Look for When Evaluating AI Consulting Firms

What to Look for When Evaluating AI Consulting Firms

23/04/2026

Evaluate AI consultancies on technical depth, delivery evidence, and knowledge transfer — not on slide decks, partnership badges, or client logo walls.
GAN vs Diffusion Model: Architecture Differences That Matter for Deployment

GAN vs Diffusion Model: Architecture Differences That Matter for Deployment

23/04/2026

GANs produce sharp output in one pass but train unstably. Diffusion models train stably but cost more at inference. Choose based on deployment constraints.
Data Quality Problems That Cause Computer Vision Systems to Degrade After Deployment

Data Quality Problems That Cause Computer Vision Systems to Degrade After Deployment

23/04/2026

CV system degradation after deployment is usually a data problem. Annotation inconsistency, domain shift, and data drift are the structural causes.

Why Most Enterprise AI Projects Fail — and How to Predict Which Ones Will

22/04/2026

Enterprise AI projects fail at 60–80% rates. Failures cluster around data readiness, unclear success criteria, and integration underestimation.

What Types of Generative AI Models Exist Beyond LLMs

22/04/2026

LLMs dominate GenAI, but diffusion models, GANs, VAEs, and neural codecs handle image, audio, video, and 3D generation with different architectures.

Proven AI Use Cases in Pharmaceutical Manufacturing Today

22/04/2026

Pharma manufacturing AI is deployable now — process control, visual inspection, deviation triage. The approach is assessment-first, not technology-first.

How to Evaluate GenAI Use Case Feasibility Before You Build

20/04/2026

Most GenAI use cases fail at feasibility, not implementation. Assess data, accuracy tolerance, and integration complexity before building.

Why Off-the-Shelf Computer Vision Models Fail in Production

20/04/2026

Off-the-shelf CV models degrade in production due to variable conditions, class imbalance, and throughput demands that benchmarks never test.

Planning GPU Memory for Deep Learning Training

16/02/2026

GPU memory estimation for deep learning: calculating weight, activation, and gradient buffers so you can predict whether a training run fits before it crashes.

CUDA AI for the Era of AI Reasoning

11/02/2026

How CUDA underpins AI inference: kernel execution, memory hierarchy, and the software decisions that determine whether a model uses the GPU efficiently or wastes it.

Deep Learning Models for Accurate Object Size Classification

27/01/2026

A clear and practical guide to deep learning models for object size classification, covering feature extraction, model architectures, detection pipelines, and real‑world considerations.

GPU vs TPU vs CPU: Performance and Efficiency Explained

10/01/2026

CPU, GPU, and TPU compared for AI workloads: architecture differences, energy trade-offs, practical pros and cons, and a decision framework for choosing the right accelerator.

AI and Data Analytics in Pharma Innovation

15/12/2025

Machine learning in pharma: applying biomarker analysis, adverse event prediction, and data pipelines to regulated pharmaceutical research and development workflows.

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.

Smarter and More Accurate AI: Why Businesses Turn to HITL

27/03/2025

Human-in-the-loop AI: how to design review queues that maintain throughput while keeping humans in control of low-confidence and edge-case decisions.

Optimising LLMOps: Improvement Beyond Limits!

2/01/2025

LLMOps optimisation: profiling throughput and latency bottlenecks in LLM serving systems and the infrastructure decisions that determine sustainable performance under load.

MLOps for Hospitals - Staff Tracking (Part 2)

9/12/2024

Hospital staff tracking system, Part 2: training the computer vision model, containerising for deployment, setting inference latency targets, and configuring production monitoring.

MLOps for Hospitals - Building a Robust Staff Tracking System (Part 1)

2/12/2024

Building a hospital staff tracking system with computer vision, Part 1: sensor setup, data collection pipeline, and the MLOps environment for training and iteration.

MLOps vs LLMOps: Let’s simplify things

25/11/2024

MLOps and LLMOps compared: why LLM deployment requires different tooling for prompt management, evaluation pipelines, and model drift than classical ML workflows.

Streamlining Sorting and Counting Processes with AI

19/11/2024

Learn how AI aids in sorting and counting with applications in various industries. Get hands-on with code examples for sorting and counting apples based on size and ripeness using instance segmentation and YOLO-World object detection.

Maximising Efficiency with AI Acceleration

21/10/2024

Find out how AI acceleration is transforming industries. Learn about the benefits of software and hardware accelerators and the importance of GPUs, TPUs, FPGAs, and ASICs.

How to use GPU Programming in Machine Learning?

9/07/2024

Learn how to implement and optimise machine learning models using NVIDIA GPUs, CUDA programming, and more. Find out how TechnoLynx can help you adopt this technology effectively.

AI in Pharmaceutics: Automating Meds

28/06/2024

Artificial intelligence is without a doubt a big deal when included in our arsenal in many branches and fields of life sciences, such as neurology, psychology, and diagnostics and screening. In this article, we will see how AI can also be beneficial in the field of pharmaceutics for both pharmacists and consumers. If you want to find out more, keep reading!

Exploring Diffusion Networks

10/06/2024

Diffusion networks explained: the forward noising process, the learned reverse pass, and how these models are trained and used for image generation.

Retrieval Augmented Generation (RAG): Examples and Guidance

23/04/2024

Learn about Retrieval Augmented Generation (RAG), a powerful approach in natural language processing that combines information retrieval and generative AI.

A Gentle Introduction to CoreMLtools

18/04/2024

CoreML and coremltools explained: how to convert trained models to Apple's on-device format and deploy computer vision models in iOS and macOS applications.

Introduction to MLOps

4/04/2024

What MLOps is, why organisations fail to move models from training to production, and the tooling and processes that close the gap between experimentation and deployed systems.

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

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: 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

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

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)

22/05/2022

How TechnoLynx built an unsupervised anomaly detection system using generative models — combining variational autoencoders, adversarial training, and custom diffusion models to detect data drift without labelled anomaly examples.

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

10/11/2020

Our client, Tasty Tech, was an organically growing start-up with a first-generation product in the dental space, and their product-market fit was validated. Read more.

Case Study - Fraud Detector Audit (Under NDA)

17/09/2020

Discover how a robust fraud detection system combines traditional methods with advanced machine learning to detect various forms of fraud!

Case Study - Accelerating Physics -Simulation Using GPUs (Under NDA)

23/01/2020

TechnoLynx used GPU acceleration to improve physics simulations for an SME, leveraging dedicated graphics cards, advanced algorithms, and real-time processing to deliver high-performance solutions, opening up new applications and future development potential.
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