We would like to use third party cookies for our own marketing purposes. We do not monetise your data otherwise.
Industries
About Us
Our Work
Blog
6/05/2026
When AMD beats NVIDIA on inference cost-per-dollar and when NVIDIA's TensorRT advantage reverses the equation.
5/05/2026
CV in pharmacy retail addresses unique challenges: regulated product tracking, controlled substance security, and planogram compliance across thousands of SKUs.
Talent intelligence uses ML to map skills, predict attrition, and identify internal mobility — but only with sufficient longitudinal employee data.
Inference infrastructure decisions should be driven by measured performance under your actual workload — vendor benchmarks rarely match production conditions.
AI-enhanced visual inspection replaces rule-based defect detection with learned representations — but requires validated training data matching production variability.
8/05/2026
How to increase GPU utilization for AI workloads: batch sizing, kernel occupancy, memory coalescing, operator fusion, and a profiling-first approach.
CPU and GPU benchmarks measure fundamentally different things. Why comparing CPU and GPU scores directly is misleading and what system-level AI benchmarks.
MLOps solves the model deployment and maintenance problem. What it is, what problems it addresses, and when an organization actually needs it versus when.
GAMP classifies software as Category 1, 3, 4, or 5 based on complexity and configurability. AI/ML systems challenge traditional category boundaries.
Multi-agent systems decompose complex tasks across specialized agents. Design principles, failure modes, and when multi-agent adds value vs complexity.
Face detection camera prerequisites: resolution minimums, angle and lighting requirements, MTCNN vs RetinaFace vs MediaPipe, and real-world false positive.
H100 GPU servers deliver peak AI performance but cost $200K+. When the investment is justified, what configurations to consider, and common procurement mistakes.
CPU vs GPU for AI is a false binary. The right question is which operations run where and why. Memory bandwidth and parallelism determine the answer.
MLOps tools span experiment tracking, model registries, pipeline orchestration, and serving. How to choose what you need without over-engineering the.
The GAMP guide provides a risk-based framework for validating automated systems in pharma. The Second Edition extends guidance to AI, agile, and cloud.
7/05/2026
When synthetic faces defeat pretrained detectors: anti-spoofing challenges, liveness detection requirements, and when custom models are unavoidable.
CUDA vs OpenCL: performance tradeoffs, portability constraints, and a practical decision framework for GPU compute API selection.
TOPS and GPU utilization both mislead AI capacity planning. When to measure compute vs memory bandwidth vs throughput, and how to pick the right metric.
Most enterprise AI projects fail before production. The causes are structural, not technical. Understanding failure patterns before starting a project.
Continuous pharma manufacturing replaces batch processing with real-time flow. AI-based process control is essential for maintaining quality in continuous.
Diffusion models surpassed GANs on FID scores for image synthesis. What metrics shifted, where GANs still win, and what it means for production image generation.
AI CCTV monitoring vs human monitoring: cost comparison, coverage capability, response time tradeoffs, and what AI handles well vs where human judgment is.
CUDA stands for Compute Unified Device Architecture. What it means technically, why it is NVIDIA-only, and how it relates to GPU programming for AI.
A meaningful AI benchmark tests what your workload actually does. The gap between standardized tests and production performance, and how to close it.
Data science team structure depends on project scale and maturity. Roles needed, common gaps, and when a team of 2 is enough vs when you need 8.
Computer system validation in pharma requires documented evidence of fitness for use. CSA now offers a risk-based alternative to full CSV for lower-risk.
The forward process in diffusion models adds noise according to a schedule. How linear, cosine, and custom schedules affect image quality and training stability.
CCTV face recognition: resolution requirements, angle and lighting challenges, false positive rates, GDPR compliance, and why production performance lags.
What a CUDA kernel is, how threads and blocks map to GPU hardware, and when custom kernels beat library calls.
GPUs scoring identically on short benchmarks can differ by 15-30% under sustained load. How stress testing exposes the limits that benchmarks miss.
AI POC requirements must be defined before development starts. Data access, success metrics, scope boundaries, and stakeholder alignment determine POC outcomes.
cGMP is the FDA's evolving standard for manufacturing quality. GMP is the broader WHO/EU framework. The 'current' modifier changes what compliance means.
What autonomous AI software engineering agents can actually do today: code generation quality, context limits, test generation, and where human oversight.
AI CCTV for building security: intrusion detection, people counting, loitering analytics, camera placement strategy, and storage and bandwidth.
What makes a GPU CUDA-capable, how CUDA compute capability tiers work, and what the architecture enables for parallel compute workloads.
Consumer benchmarks measure the wrong thing for AI. AI benchmarks test the wrong workloads. What each GPU benchmark tool measures and what to use instead.
AI workforce engagement requires training, process redesign, and change management. How organisations build AI literacy and manage the automation transition.
cGMP pharmaceutical regulations define minimum quality standards for drug manufacturing. Compliance requires documentation, process control, and personnel.
AI agent patterns—ReAct, Plan-and-Execute, Reflection—solve different failure modes. Choosing the right pattern determines reliability more than model.
Wired CCTV systems for AI analytics need more than high resolution. Codec support, edge processing, and integration architecture determine analytics quality.
How to verify TensorFlow GPU detection with tf.config.list_physical_devices, diagnose CUDA version mismatches, driver issues, and common failure modes.
Benchmark scores and real AI performance differ by 20-50%. How to test in a way that predicts actual workload behaviour rather than lab conditions.
AI strategy consulting ranges from genuine capability assessment to repackaged hype. What a useful engagement delivers, and the signals that distinguish.
Automated visual inspection in pharma uses computer vision to detect defects in vials, syringes, and tablets — faster and more consistently than human.
Agentic AI is moving from demos to production. What's deployed today, what's still research, and how to evaluate claims about autonomous AI systems.
Build automated visual inspection systems that work: hardware setup, model selection (classification vs detection vs segmentation), and managing.
Free and cheap cloud GPUs have real limits. Comparing tier costs, quota, and what to expect from spot instances for AI training and inference.
AMD vs Intel CPU performance for AI workloads varies by up to 3x depending on model architecture and software stack. No single 'better' answer exists.
AI POC success requires pre-defined business criteria, not model accuracy. How to scope a 6-week AI proof of concept that produces a real go/no-go.
Aseptic manufacturing prevents microbial contamination during sterile drug production. AI monitoring addresses the environmental control gaps humans miss.
Agent-based modeling simulates populations of interacting entities. When it's the right choice over LLM-based agents and how to combine both approaches.
4K security cameras for AI analytics: bandwidth and storage costs, where higher resolution improves results, compression artifacts and AI accuracy.
Low-profile GPUs for AI inference are constrained by power and cooling. Which models fit, what performance to expect, and when to choose a different form factor.
AI orchestration coordinates multiple models through defined handoff protocols. Without it, multi-agent systems produce compounding inconsistencies.
AI shifts pharma compliance from periodic manual audits to continuous automated validation — catching deviations in hours instead of months.
Production agent development follows a narrow-first pattern: single tool, single goal, deterministic fallback — then widen incrementally with observability.
Enterprise AI search quality depends on chunking strategy and retrieval pipeline design more than on the LLM. Poor retrieval + powerful LLM = confident wrong answers.
Tensor parallelism splits operations across GPUs (low latency, high bandwidth need). Pipeline parallelism splits layers (tolerates lower bandwidth, adds bubble overhead).
New AI-driven monitoring systems detect contamination risk in aseptic filling by analysing environmental and process data continuously rather than via batch sampling.
Facial recognition accuracy drops 10–40% between controlled enrollment conditions and production CCTV due to angle, lighting, and resolution.
Most AI agent demos work on curated inputs. Production viability requires error handling, fallback chains, and observability that demos never test.
AI consulting for SMBs must start with data audit and process mapping — not model selection — because most failures stem from insufficient data infrastructure.
Inference efficiency is performance-per-watt and cost-per-inference, not raw FLOPS. Batch size, precision, and memory bandwidth determine throughput.
AI inference throughput depends primarily on tensor core utilisation, not CUDA core count. Tensor core generation determines supported precision formats.
Store analytics CV must distinguish 'detected' from 'measured with business-decision confidence.' Most deployments conflate the two.
Pharma supply chain AI delivers measurable ROI in three areas: serialisation verification, cold-chain anomaly prediction, and visual inspection automation.
CUDA compute capability determines which tensor core operations and precision formats a GPU supports — not just whether CUDA runs.
Profiling must precede GPU optimisation. Memory bandwidth fixes typically deliver 2–5× more impact than compute-bound fixes for AI workloads.
MLOps consulting should transfer capability, not create dependency. The exit criteria matter more than the entry scope.
An LLM agent adds tool use, memory, and planning loops to a base model. Agent reliability depends on orchestration more than model benchmark scores.
BF16 trades mantissa precision for dynamic range. The choice depends on whether your workload is gradient-dominated or activation-precision-dominated.
CV-based loss prevention must handle thousands of SKUs under variable lighting. Single-model approaches produce unactionable alert volumes at scale.
GxP is a family of regulations — GMP, GLP, GCP, GDP — each applying different validation requirements to AI systems depending on lifecycle role.
GPU parallelism exploits thousands of simple cores for data-parallel workloads. The execution model differs fundamentally from CPU thread-level parallelism.
4/05/2026
TOPS measures theoretical throughput at one precision. It ignores memory bandwidth, software overhead, and workload fit — making it a poor performance predictor.
IVA shifts surveillance alerting from pixel-change detection to behaviour understanding. But only modular pipeline architectures deliver this in practice.
No single AI agent excels at all task types. The best choice depends on whether your workflow is structured or unstructured.
A100 rental pricing varies 2–5× between providers depending on commitment length, region, and availability. Here is what the market looks like.
MLOps tooling is consolidating around integrated platforms. The operational complexity shifts from integration to configuration and governance.
2/05/2026
A pharma AI POC that survives GxP validation: five instrumentation choices made at week one, removing the 6–9 month re-derivation at validation handover.
Selecting an agent framework for partial on-device inference: four axes that decide whether a desktop-class framework survives the edge-target boundary.
1/05/2026
Cross-platform TTS to iOS, Android and browser stays consistent only if compression is decided at training time — distill once, export to ONNX.
Generative models trained on normal frames detect rare video anomalies without labelled anomaly data — reconstruction error is the score.
30/04/2026
Operators stop trusting CV alerts when the pipeline is opaque. Observable, modular CCTV pipelines decompose decisions into auditable stages.
Retail CV deployments meet products outside the training catalogue. The architectural choice: silent misclassification or a designed review loop.
29/04/2026
Client-side ML misses latency targets when the device capability baseline is set after architecture selection rather than before. Sequence matters.
Graceful degradation in production SKU recognition is an architectural property: predictable automation rate as the catalogue grows.
28/04/2026
Distillation and quantisation both shrink models for edge inference, but for three-or-more platforms only distillation keeps quality consistent.
Naive GPU porting of sequential RF simulation delivers modest gains. Algorithmic redesign to expose parallelism turns multi-day runtimes into hours.
Surveillance false alarms are an architecture problem, not a sensitivity setting. Modular pipelines reduce them; monolithic ones cannot.
CV models that pass accuracy tests at 500 SKUs fail in production above 1,000 — not from one cause but from four simultaneous failure axes.
27/04/2026
Engineering tasks have known solutions and predictable timelines. Research questions have uncertain outcomes. Conflating the two causes project failure.
MLOps keeps AI models working after deployment. Start with monitoring, versioning, and retraining pipelines — not full platform adoption.
A working GenAI prototype is not production-ready. It still needs evaluation pipelines, guardrails, cost controls, latency optimisation, and monitoring.
26/04/2026
Build internal teams for sustained advantage. Hire consultants for speed, specialisation, and knowledge transfer. Most organisations need both.
AI readiness is about data infrastructure, organisational capability, and governance maturity — not technology. Assess all three before committing.
Agent frameworks differ on observability, tool integration, error recovery, and readiness. LangGraph, AutoGen, and CrewAI target different needs.
Custom CV models are justified when the domain is specialised and off-the-shelf accuracy is insufficient. Otherwise, customisation adds waste.
Source-level portability is not performance portability. Competitive speed across GPU vendors needs architecture-aware abstraction and per-target tuning.
25/04/2026
A structured AI engagement moves through assessment, POC, production build, and handoff — with decision gates, not open-ended retainers.
Multi-agent AI decomposes tasks across specialised agents. Conflicting plans, hallucinated handoffs, and unbounded loops are the production risks.
Edge CV trades accuracy for latency and bandwidth savings. Quantisation, model selection, and hardware matching determine whether the trade-off works.
Cloud GPU suits variable, short-term workloads. On-premise is cheaper for sustained utilisation above 60%. The break-even is calculable, not philosophical.
Annex 11 governs computerised systems in EU pharma manufacturing. Its data integrity requirements and AI implications are more specific than teams assume.
24/04/2026
An AI POC should prove feasibility, not capability. It needs four sections: structure, success criteria, ROI measurement, and packageable value.
Generative AI produces output on request. Agentic AI takes autonomous multi-step actions toward a goal. The core difference is execution autonomy.
Retail CV ROI comes from shrinkage reduction, planogram compliance, and checkout automation — not AI dashboards. Measure what changes operationally.
Inference latency optimisation targets model compilation, batching, and memory management — not hardware speed. TensorRT and quantisation are key levers.
GAMP 5 categories were designed for deterministic software. AI/ML systems require the Second Edition's risk-based approach and continuous validation.
23/04/2026
Evaluate AI consultancies on technical depth, delivery evidence, and knowledge transfer — not on slide decks, partnership badges, or client logo walls.
GANs produce sharp output in one pass but train unstably. Diffusion models train stably but cost more at inference. Choose based on deployment constraints.
CV system degradation after deployment is usually a data problem. Annotation inconsistency, domain shift, and data drift are the structural causes.
Kernel tuning improves constant factors. Algorithmic restructuring changes complexity class. Identify your bottleneck type before committing effort.
CV-based pharma QC inspection is a production engineering problem, not a model accuracy problem. It requires data, validation, and pipeline design.
22/04/2026
Enterprise AI projects fail at 60–80% rates. Failures cluster around data readiness, unclear success criteria, and integration underestimation.
LLMs dominate GenAI, but diffusion models, GANs, VAEs, and neural codecs handle image, audio, video, and 3D generation with different architectures.
A production CV pipeline is a system architecture problem, not a model accuracy problem. Modular design enables debugging and component-level maintenance.
GPU profiling separates compute-bound from memory-bound kernels. Nsight Compute roofline analysis shows where a kernel sits and what would move it.
Pharma manufacturing AI is deployable now — process control, visual inspection, deviation triage. The approach is assessment-first, not technology-first.
21/04/2026
GenAI project failures cluster around scope inflation, evaluation gaps, and integration underestimation. The patterns are predictable and preventable.
Most GPU workloads use 30–50% of available compute. Without profiling, the waste is invisible. Bandwidth, occupancy, and serialisation are the root causes.
Machine vision is deterministic and auditable. Computer vision is adaptive and generalisable. The choice depends on defect complexity, not preference.
GxP applies to AI software that affects product quality, safety, or data integrity — not to every system in a pharma facility. The boundary matters.
Pharmaceutical batch failures cost waste, rework, and regulatory exposure. AI-based process control prevents the failure classes behind most rejections.
20/04/2026
Most GenAI use cases fail at feasibility, not implementation. Assess data, accuracy tolerance, and integration complexity before building.
CUDA delivers the deepest optimisation on NVIDIA hardware. OpenCL and SYCL offer portability. Choose based on lock-in tolerance and performance needs.
Off-the-shelf CV models degrade in production due to variable conditions, class imbalance, and throughput demands that benchmarks never test.
Pharma AI adoption stalls from regulatory misperception, scope inflation, and transformation assumptions. Each delay has a measurable manufacturing cost.
CSA and full CSV are different validation approaches for AI in pharma. The right choice depends on system risk, not regulatory habit.
17/04/2026
Performance per dollar. Tokens per watt. Cost per request. These sound like the same thing said differently, but they measure genuinely different dimensions of AI infrastructure economics. Conflating them leads to infrastructure decisions that optimize for the wrong objective.
Precision isn't just a numerical setting — it's an economic one. Choosing FP8 over BF16, or INT8 over FP16, changes throughput, latency, memory footprint, and power draw simultaneously. For inference at scale, these changes compound into significant cost differences.
You can't run FP8 inference on hardware that doesn't have FP8 tensor cores. Precision format decisions are conditional on the accelerator's architecture — its tensor core generation, native format support, and the efficiency penalties for unsupported formats.
Capacity planning built on peak performance numbers over-provisions or under-delivers. Real infrastructure sizing requires steady-state throughput — the predictable, sustained output the system actually delivers over hours and days, not the number it hit in the first five minutes.
16/04/2026
A benchmark result starts with full context — workload, software stack, measurement conditions. By the time it reaches a procurement deck, all that context is gone. The failure mode is not wrong benchmarks but context loss during propagation.
High-value AI hardware decisions need traceable evidence, not slide-deck bullet points. When benchmarks are documented with methodology, assumptions, and limitations, they become auditable institutional evidence — defensible under scrutiny and revisitable when conditions change.
Two benchmark scores can only be compared if they share a declared methodology — the same workload, precision, measurement protocol, and reporting conditions. Without that contract, the comparison is arithmetic on numbers of unknown provenance.
Hardware selection is a multivariate decision under uncertainty — not a score comparison. This framework walks through the steps: defining the decision, matching evaluation to deployment, measuring what predicts production, preserving tradeoffs, and building a repeatable process.
Before a benchmark score informs a purchase, it has already shaped what gets optimized, what gets reported, and what the organization considers important. Benchmarks function as decision infrastructure — and that influence deserves more scrutiny than the number itself.
Reduced precision does not produce a uniform accuracy penalty. Sensitivity depends on the task, the metric, and the evaluation setup — and accuracy impact cannot be assumed without measurement.
Numerical precision is an explicit design parameter in AI systems, not a moral downgrade in quality. This article reframes precision as a representation choice with intentional trade-offs, not a concession made reluctantly.
Not every multiplication deserves 32 bits. Mixed precision works because neural network computations have uneven numerical sensitivity — some operations tolerate aggressive precision reduction, others don't — and the performance gains come from telling them apart.
Throughput and latency are different objectives that often compete for the same resources. This article explains the trade-off, why batch size reshapes behavior, and why percentiles matter more than averages in latency-sensitive systems.
When someone says 'quantize the model,' the instinct is to hear 'degrade the model.' That framing is wrong. Quantization is controlled numerical approximation — a deliberate engineering trade-off with bounded, measurable error characteristics — not an act of destruction.
15/04/2026
The utilization percentage in nvidia-smi reports kernel scheduling activity, not efficiency or throughput. This article explains the metric's exact definition, why it routinely misleads in both directions, and what to pair it with for accurate performance reads.
FP8 is not just 'half of FP16.' Each numerical format encodes a different set of assumptions about range, precision, and risk tolerance. Choosing between them means choosing operating regimes — different trade-offs between throughput, numerical stability, and what the hardware can actually accelerate.
AI systems rarely operate at peak. This article defines the peak vs. steady-state distinction, explains when each regime applies, and shows why evaluations that capture only peak conditions mischaracterize real-world throughput.
Drivers, runtimes, frameworks, and libraries define the execution path that determines GPU throughput. This article traces how each software layer introduces real performance ceilings and why version-level detail must be explicit in any credible comparison.
Is 100% GPU utilization bad? Will it damage the hardware? Should you be worried? For datacenter AI workloads, sustained high utilization is normal — and the anxiety around it usually reflects gaming-era intuitions that don't apply.
The word 'realistic' gets attached to benchmarks freely, but real AI workloads have properties that synthetic benchmarks structurally omit: variable request patterns, queuing dynamics, mixed operations, and workload shapes that change the hardware's operating regime.
'Same GPU' does not imply the same performance. This article explains why system configuration, software versions, and execution context routinely outweigh nominal hardware identity.
A GPU that excels at training may disappoint at inference, and vice versa. Training and inference stress different system components, follow different scaling rules, and demand different optimization strategies. Treating them as interchangeable is a design error.
When an AI workload underperforms, attribution is the first casualty. Hardware blames software. Software blames hardware. The actual problem lives in the gap between them — and no single team owns that gap.
AI performance is an emergent property of hardware, software, and workload operating together. This article explains why outcomes cannot be attributed to hardware alone and why the stack is the true unit of performance.
14/04/2026
Every GPU has a physical ceiling that sits below its theoretical peak. Power limits, thermal throttling, and transient boost clocks mean that the performance you read on the spec sheet is not the performance the hardware sustains. The physics always wins.
That impressive throughput number from the first five minutes of a training run? It probably won't hold. AI workload performance shifts over time due to warmup effects, thermal dynamics, scheduling changes, and memory pressure. Understanding why is the first step toward trustworthy measurement.
Why is it so hard to switch away from CUDA? Because the lock-in isn't in the API — it's in the ecosystem. Libraries, tooling, community knowledge, and years of optimization create switching costs that no hardware swap alone can overcome.
CPU overhead, memory bandwidth, PCIe topology, and host-side scheduling routinely limit what a GPU can deliver — even when the accelerator itself has headroom. This article maps the non-GPU bottlenecks that determine real AI throughput.
Spec sheets, leaderboards, and vendor numbers cannot substitute for empirical measurement under your own workload and stack. Defensible performance conclusions require representative execution — not estimates, not extrapolations.
When GPU utilization drops below expectations, the cause usually isn't the GPU itself. This article traces common bottleneck patterns — host-side stalls, memory-bandwidth limits, pipeline bubbles — that create the illusion of idle hardware.
AI GPU performance is multi-dimensional and workload-dependent. This article explains why scalar rankings collapse incompatible objectives and why 'best GPU' questions are structurally underspecified.
A benchmark result is not a hardware measurement — it is an execution measurement. The GPU, the software stack, and the workload all contribute to the number. Reading it correctly requires knowing which parts of the system shaped the outcome.
GPU spec sheets describe theoretical limits. This article explains why real AI performance is an execution property shaped by workload, software, and sustained system behavior.
19/03/2026
NVIDIA data centre GPUs explained: architecture differences, when to choose them over consumer GPUs, and how workload type determines the right GPU configuration in a data centre.
16/03/2026
CUDA and OpenCL compared for GPU programming: programming models, memory management, tooling, ecosystem fit, portability trade-offs, and a practical decision framework.
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.
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.
28/01/2026
A practical comparison of Vulkan, OpenCL, SYCL and CUDA, covering portability, performance, tooling, and how to pick the right path for GPU compute across different hardware vendors.
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.
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.
7/01/2026
GPU computing in drug discovery: how parallel workloads accelerate molecular simulation, docking calculations, and deep learning models for compound property prediction.
6/01/2026
Where GPUs are essential in healthcare AI: medical image processing, genomic workloads, and real-time inference that CPU-only architectures cannot sustain at production scale.
16/12/2025
AI in biotech research: how machine learning accelerates compound screening, genomic analysis, and experimental design decisions in biological research pipelines.
15/12/2025
Machine learning in pharma: applying biomarker analysis, adverse event prediction, and data pipelines to regulated pharmaceutical research and development workflows.
12/12/2025
AI for rare disease diagnosis: how small dataset constraints shape model selection, transfer learning strategies, and the clinical validation requirements.
10/11/2025
Why computer vision systems trained on benchmarks fail on real inputs, and how attention mechanisms, context modelling, and multi-scale features close the gap.
7/11/2025
Neural network visualisation: how activation maps, layer inspection, and feature attribution reveal what a model has learned and where it will fail.
6/11/2025
Learn how visual computing transforms life sciences with real-time analysis, improving research, diagnostics, and decision-making for faster, accurate outcomes.
21/10/2025
Learn how AI-driven aseptic operations help pharmaceutical manufacturers reduce contamination, improve risk assessment, and meet FDA standards for safe, sterile products.
20/10/2025
See how AI-powered visual quality control ensures safe, compliant, and high-quality pharmaceutical packaging across a wide range of products.
15/10/2025
See how AI and generative AI help pharmaceutical companies optimise manufacturing processes, improve product quality, and ensure safety and efficacy.
25/09/2025
What the 2‑D barcode and seal on your medicine mean, how pharmacists scan packs, and why these checks stop fake medicines reaching you.
24/09/2025
A clear, GxP‑ready guide to the EU AI Act for pharma and medical devices: risk tiers, GPAI, codes of practice, governance, and audit‑ready execution.
23/09/2025
Reduce batch effects in Cell Painting. Standardise assays, adopt OME‑Zarr, and apply robust harmonisation to make high‑content screening reproducible.
22/09/2025
Raise slide quality and trust in AI for digital pathology with robust WSI validation, automated QC, and explainable outputs that fit clinical workflows.
19/09/2025
Make AI systems validation‑ready across GxP. GMP, GCP and GLP. Build secure, audit‑ready workflows for data integrity, manufacturing and clinical trials.
17/09/2025
Edge imaging transforms cell & gene therapy manufacturing with real‑time monitoring, risk‑based control and Annex 1 compliance for safer, faster production.
15/09/2025
AI enhances genetic variant interpretation by analysing DNA sequences, de novo variants, and complex patterns in the human genome for clinical precision.
11/09/2025
Improve quality and safety in sterile injectable manufacturing with AI‑driven visual inspection, real‑time control and cost‑effective compliance.
5/09/2025
AI helps pharma teams predict clinical trial risks, side effects, and deviations in real time, improving decisions and protecting human subjects.
1/09/2025
Generative AI transforms pharma by streamlining compliance, drug discovery, and documentation with AI models, GANs, and synthetic training data for safer innovation.
27/08/2025
AI helps pharma teams improve compliance, reduce risk, and manage quality in clinical trials and manufacturing with real-time insights.
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.
12/05/2025
Multi-object tracking in production: handling occlusion, re-identification, and real-time latency constraints in industrial and retail camera systems.
24/04/2025
Integrating computer vision into assembly lines: inspection system design, detection accuracy targets, and edge deployment considerations for manufacturing environments.
10/04/2025
Video pipeline optimisation: how encoding, transmission, and decoding decisions determine real-time computer vision latency and processing throughput at scale.
9/04/2025
GPU optimisation for real-time rendering workloads: profiling GPU-bound bottlenecks, memory bandwidth constraints, and frame scheduling decisions in XR systems.
31/03/2025
Discover how Markov chains power Generative AI models, from text generation to computer vision and AR/VR/XR. Explore real-world applications!
28/03/2025
See how augmented reality entertainment is changing film, gaming, and live events with digital elements, AR apps, and real-time interactive experiences.
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.
24/03/2025
Quality control with computer vision: inspection pipeline design, defect detection architectures, and the measurement factors that determine false-reject rates in production.
17/03/2025
Computer vision for inventory counting and tracking: how shelf-state monitoring, object detection, and anomaly detection reduce manual audit overhead in warehouses and retail.
Explainability in computer vision: how saliency maps, attention visualisation, and interpretable architectures make CV models auditable and correctable in production.
10/02/2025
Real-time face detection in production: CNN architecture choices, detection pipeline design, and the latency constraints that determine deployment feasibility.
2/01/2025
LLMOps optimisation: profiling throughput and latency bottlenecks in LLM serving systems and the infrastructure decisions that determine sustainable performance under load.
10/12/2024
Hierarchical SKU classification using DINO embeddings and few-shot learning — above 95% accuracy at ~1k classes, above 83% at ~2k.
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.
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.
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.
20/11/2024
Browser-deployed face quality classifier rebuilt around a single multiclassifier, WebGL pixel capture, and explicit device-capability gating.
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.
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.
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.
16/08/2024
CUDA, OpenCL, Metal, and Vulkan compared for GPU compute: when to use each API and what the trade-offs are for different application targets and hardware platforms.
16/07/2024
Discover why GPUs are essential in AI. Learn about their role in machine learning, neural networks, and deep learning projects.
15/07/2024
In-cart perception for autonomous retail checkout: detection, tracking, adaptive FPS sampling, and a session-scoped cart-state model.
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.
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!
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.
6/05/2024
How computer vision powers shelf monitoring, customer flow analysis, and checkout automation in retail environments — and what integration actually requires.
3/05/2024
Computer vision in medical imaging: how AI systems accelerate screening and diagnostic workflows while managing the false-positive rates that determine clinical acceptance.
23/04/2024
Learn about Retrieval Augmented Generation (RAG), a powerful approach in natural language processing that combines information retrieval and generative AI.
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.
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.
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.
16/11/2023
Let's talk about how artificial intelligence, coupled with computer vision, is reshaping manufacturing processes!
19/10/2023
Computer vision in manufacturing: how inspection systems detect defects, verify assembly, and measure dimensional tolerances in real-time production environments.
15/10/2023
Camera-based barcode pipeline for in-cart capture: YOLO localisation, ensemble decoding, multi-frame polling — 86.7% vs Dynamsoft 80%.
6/10/2023
With the hype of generative AI, all of us had the urge to build a generative AI application or even needed to integrate it into a web application.
22/06/2023
A new groundbreaking model developed by researchers at the MIT utilizes machine learning and AI to accelerate the drug discovery process.
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.
4/05/2023
Listen what our CEO has to say about the limitations of AI-as-a-Service.
26/04/2023
Traditionally, drug discovery is a slow and expensive process that involves trial and error experimentation.
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.
1/02/2023
Most GPU-naïve companies would like to think of GPUs as CPUs with many more cores and wider SIMD lanes, but unfortunately, that understanding is missing some crucial differences.
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.
4/01/2023
AI Research from the University of Maryland investigating the cramming challenge for Training a Language Model on a Single GPU in one day.
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.
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.
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.
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!
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.
17/09/2020
Discover how a robust fraud detection system combines traditional methods with advanced machine learning to detect various forms of fraud!
15/04/2020
TechnoLynx built a CUDA-based H.264 encoder on a Jetson Nano-class embedded GPU for an automotive edge startup, targeting ≤5% CPU usage across 4+ simultaneous 1080p/30fps streams. Delivered ~24 FPS — more than double the prior baseline — and a ~3.6% average compression gain in low-QP benchmark conditions.
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.
