Visual Computing for
Life Sciences

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10×–300×
Faster Simulation Speedups

GPU-accelerated computing for R&D modeling

30–40%
Lower Cost Reduction

Predictive maintenance cuts downtime expenses

Up to 90%
Shorter Development Time

Digital twins accelerate scale-up and process design

100%
Coverage, Quality Inspection

AI vision ensures 100% inspection zero label errors

Accelerating Innovation with Regulatory Compliance

TechnoLynx helps pharma and biotech companies boost R&D, optimise manufacturing, and ensure quality using advanced AI and high-performance computing. We solve industry challenges-like contamination prevention and faster drug discovery-while meeting strict regulations. Our solutions deliver faster market entry, higher yields, fewer failures, and stronger compliance.

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What We Deliver

TechnoLynx combines technical innovation (AI, machine learning, computer vision, GPU computing) with business impact (cost and time savings, risk reduction, productivity).

AI-Powered Aseptic Operations

TechnoLynx combines technical innovation (AI, machine learning, computer vision, GPU computing) with business impact (cost and time savings, risk reduction, productivity).

R&D Acceleration

HPC and AI accelerate simulations by 10-300×, enabling faster drug discovery, quicker breakthroughs, and reduced time-to-clinic.

Manufacturing Optimisation

Machine learning maximises yield and minimises downtime, improving output and consistency. Predictive analytics enable proactive maintenance, cutting downtime and costs by ~30-40%.

Digital Twins

Virtual simulations and digital replicas enable risk-free experimentation, reducing commissioning time and shortening development cycles by up to 90%.

Automated Visual Quality Control

AI vision systems inspect every product and package, ensuring 100% quality control, zero defects, and preventing costly recalls.

Data Mining for Innovation

AI analytics uncover patterns in vast datasets, guiding insight-driven innovation, accelerating research, and enabling faster product development and IP creation.

How It Works:

The Technology Behind the Impact

Solves

Approach

Example

Areas of Expertise

Cleanroom CV & Annex 1 compliance
Protocol-deviation early warning (RBQM)
CGT in-process imaging at the edge
GxP data pipelines & validation (CSV)
MES/EBR/SCADA integration

Our Technological Capabilities
Are Centred Around Three Core Pillars

Computer Vision Services

Transform your processes with advanced visual recognition and analysis. Our services feature expertise in classical computer vision, human-supervised system design for legal compliance, video pipeline optimisation with tools like FFmpeg, custom adaptable models, and explainable AI for ethical transparency.

Pharma

Generative AI

We are leaders in generative and agentic AI — with optimised inference for faster deployments, bias-mitigated ethical AI, autonomous agent workflows for regulated workflows, and advanced simulation and prototyping capabilities.

DNA

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.

Camera

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 Lightning
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

Compliance & Life Sciences FAQ

How is the TechnoLynx system validated for GxP environments?

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We deliver "validation-ready" AI solutions designed for strict regulatory scrutiny. Our validation support includes:

  • IQ/OQ Support: Complete documentation for Installation and Operational Qualification.
  • Regulatory Alignment: Full compliance with Annex 11 (EU) and 21 CFR Part 11 (FDA) for electronic records.
  • Data Integrity: Explainable AI (xAI) ensures every decision is traceable and auditable.

How does cleanroom monitoring ensure employee privacy?

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TechnoLynx employs "Privacy by Design" to monitor processes, not people. Our systems ensure compliance without using biometric identification. All data handling is strictly aligned with GDPR and GMP data integrity standards, ensuring human supervision without compromising individual anonymity.

Can AI models adapt to specific assays and lab hardware?

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Yes, our models are hardware-agnostic and assay-specific. We specialize in custom model adaptation, tuning our vision algorithms to work with your specific imaging hardware, lab conditions, and proprietary assay requirements to ensure robust performance across diverse platforms.

How do you ensure AI decisions are transparent and explainable?

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We provide a comprehensive xAI Governance Toolkit for regulatory review. This includes:

  • Model Cards: Detailed documentation of model lineage and training.
  • Feature Attribution: Visualizing which data points influenced an AI decision.
  • Fairness Metrics: Ensuring unbiased outputs for QA and regulatory audits.

How do you manage latency for real-time visual inspection?

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We achieve deterministic low latency through GPU performance engineering. By leveraging asynchronous compute and multi-GPU orchestration, we provide high-throughput visual inspection that meets real-time production requirements without compromising validation controls.

How does TechnoLynx manage data integrity and audit readiness?

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Our data management follows the ALCOA+ principles (Attributable, Legible, Contemporaneous, Original, and Accurate). We provide:

  • Digitally signed audit trails and versioned configurations.
  • Role-based access control (RBAC).
  • Exportable compliance snapshots for rapid regulatory submissions.

Is AI ready for GxP-regulated pharma manufacturing today?

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Yes — waiting is the strategic error. Several AI use cases in pharma manufacturing already have proven validation pathways under CSA, CSV, GAMP 5 second edition, and Annex 11; the regulatory perimeter is often narrower than internal teams assume, and well-scoped systems can be deployed without expanding the audit surface unnecessarily — see why pharma delay costs more than adoption, CSA vs CSV for AI systems, and proven AI use cases in pharma manufacturing today.

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)

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