# TechnoLynx

> TechnoLynx is a London-based AI engineering consultancy, founded in 2019. It sells five outcome-owned engineering packs, each scoped to one problem — cost, portability, reliability, or trust — rather than horizontal GPU, computer-vision, or generative-AI capability categories. Every engagement ends in something the client keeps and can re-run: a benchmark, an eval script, a deployment runbook, or a readiness scorecard. The team is led by Balázs Keszthelyi, who built the first OpenCL benchmark adopted by major GPU vendors and architected the VC-6 codec.

## Pillars
- Faster, cheaper production AI (cost)
- Edge & on-device AI (portability)
- Reliable production AI (reliability)
- AI governance and trust (trust)


## Services

- [Services Overview](https://www.technolynx.com/services/): Five productised engineering packs across four pillars: cost, portability, reliability, trust.
- [Inference Cost-Cut Pack](https://www.technolynx.com/services/inference-cost-cut-pack/): Cut inference cost and p95 latency on the workload you actually run: an Audit on-ramp plus a 4-8 week Optimisation Sprint.
- [AI Porting & Deployment Pack](https://www.technolynx.com/services/ai-porting-deployment-pack/): Get AI workloads running on novel silicon, edge boards, embedded targets, or the browser, with a reproducible runbook.
- [Production AI Monitoring Harness](https://www.technolynx.com/services/production-ai-monitoring-harness/): Build the eval, regression, drift, and slice-metric harness that turns a working AI demo into a system on-call can defend.
- [LLM Selection Pack](https://www.technolynx.com/services/llm-selection-pack/): Build the LLM eval suite and risk-and-comparison report your approval committee can re-run on every model swap.
- [AI Readiness Scorecard](https://www.technolynx.com/services/ai-readiness-scorecard/): Score your AI programme against a named published rubric (NIST AI RMF, ML Test Score, HIPAA/GxP) with an evidence trail.


## Technologies

- [Technologies Overview](https://www.technolynx.com/technologies): TechnoLynx's underlying engineering disciplines, from GPU performance to production AI trust and reliability.
- [GPU Performance Engineering](https://www.technolynx.com/gpu): Algorithm redesign for GPU acceleration, cross-platform porting (CUDA, OpenCL, Vulkan, Metal), and full-stack performance tuning.
- [Computer Vision](https://www.technolynx.com/computer-vision): Object detection, tracking, recognition, anomaly detection, and production-grade CV pipelines that work beyond demo conditions.
- [Generative AI](https://www.technolynx.com/generative-ai): Custom generative models (GANs, VAEs, diffusion), LLM-based agentic workflows, and style transfer — generative AI beyond just LLMs.
- [Production AI Reliability](https://www.technolynx.com/technologies/production-ai-reliability/): The discipline and artefacts that turn a working AI deployment into a system on-call can defend.
- [AI Governance and Trust](https://www.technolynx.com/technologies/ai-governance-and-trust/): Approval evidence packs that survive an external auditor, a procurement committee, or a regulator.


## Industries

- [Industries Overview](https://www.technolynx.com/industries): Domain failure classes connected to engineering decisions, across life sciences, retail, media/telecom, and AI infrastructure.
- [Life Sciences](https://www.technolynx.com/life-sciences): Medical-imaging validation and HIPAA/GxP-ready readiness scoring for clinical AI workflows.
- [Retail](https://www.technolynx.com/industries/retail/): Shelf-execution and stock-out eval harnesses, plus visual-search and product-discovery pipeline cost-cuts.
- [Media & Telecom](https://www.technolynx.com/industries/media-telecom/): Video-pipeline cost-cuts, eval harnesses for content systems, and operational-anomaly detection.
- [AI Infrastructure & SaaS](https://www.technolynx.com/industries/ai-infrastructure-saas/): Production AI cost-cuts, eval harnesses, runtime porting, and LLM evidence packs for AI-native product teams.
- [Manufacturing & Automotive](https://www.technolynx.com/industries/manufacturing-automotive/): Industrial CV inspection validation and automotive perception engineering evidence.
- [Broadcast](https://www.technolynx.com/broadcast): GPU-accelerated signal simulation, codec optimisation, and AI analytics for telecom and media.
- [Surveillance](https://www.technolynx.com/surveillance): AI-powered video analytics with false-alarm reduction and GDPR-compliant processing.
- [Telecommunications](https://www.technolynx.com/telecommunications): RF simulation acceleration, custom codec engineering, and AI-driven network analytics.


## Case Studies

A representative sample. The full set is at /case-studies.

- [GPU Porting from OpenCL to Metal (V-Nova)](https://www.technolynx.com/post/case-study-GPU-porting-from-opencl-to-metal): Ported a GPU video processing pipeline from OpenCL to Metal for Apple M1/M2.
- [Performance Modelling of AI Inference on GPUs](https://www.technolynx.com/post/case-study-performance-modelling-of-ai-inference-on-gpus): Reduced inference costs for trained neural networks and real-time applications.
- [Accelerating Physics Simulation Using GPUs](https://www.technolynx.com/post/case-study-accelerating-physics-simulation-using-gpus): GPU acceleration of physics simulations for an SME.
- [CloudRF Signal Propagation and Tower Optimisation](https://www.technolynx.com/post/case-study-cloudrf-signal-propagation-and-tower-optimisation): GPU-accelerated RF propagation and tower placement simulation.
- [Multi-Target Multi-Camera Tracking](https://www.technolynx.com/post/case-study-multi-target-multi-camera-tracking): AI-powered multi-target tracking across non-overlapping CCTV cameras.
- [Text-to-Speech Inference Optimisation on Edge](https://www.technolynx.com/post/case-study-text-to-speech-inference-optimisation-on-edge): Real-time Kazakh TTS using ONNX and edge optimisation.
- [Large-Scale SKU Product Recognition](https://www.technolynx.com/post/case-study-large-scale-sku-product-recognition): Retail SKU recognition at operational scale, across thousands of visually similar classes.
- [AI-Generated Dental Simulation](https://www.technolynx.com/post/case-study-ai-generated-dental-simulation): Custom generative model for dental treatment visualisation.
- [Generative AI for Stock Market Prediction](https://www.technolynx.com/post/case-study-generative-ai-for-stock-market-prediction): Sentiment analysis and LLMs for real-time trading signal generation.


## LynxBenchAI

LynxBenchAI is TechnoLynx's AI hardware benchmarking methodology, with a live provisional leaderboard. It measures performance as a property of the complete hardware-and-software stack, sustained under realistic load, reported per precision, with bounded optimisation. The aim is procurement-grade comparability, not a marketing leaderboard.

- [LynxBenchAI Overview](https://www.technolynx.com/lynxbench-ai/): Methodology and product landing page.
- [Get Started with LynxBenchAI](https://www.technolynx.com/lynxbench-ai/get-started/): Install and run the Personal Edition benchmark on Linux or Windows via WSL2.
- [Leaderboard](https://www.technolynx.com/lynxbench-ai/leaderboard/): Global-score leaderboard: AI hardware ranked by GT, with per-category (training, inference, compute) breakdown. Provisional results.
- [Hardware Directory](https://www.technolynx.com/lynxbench-ai/directory/): A-Z directory of every device benchmarked by LynxBenchAI, grouped by hardware type. Provisional results.
- [Why Spec-Sheet Benchmarking Fails for AI](https://www.technolynx.com/post/why-spec-sheet-benchmarking-fails-for-ai): Peak TFLOPS and synthetic scores do not predict production AI performance.
- [Benchmarks Measure Execution, Not Hardware](https://www.technolynx.com/post/benchmarks-measure-execution-not-hardware): A benchmark result is a property of the hardware-and-software pair, not the silicon alone.
- [Performance Emerges from the Hardware-Software Stack](https://www.technolynx.com/post/performance-emerges-from-the-hardware-software-stack): Driver, runtime, framework, and kernel implementation determine what the hardware delivers.
- [Peak vs Steady-State Performance in AI](https://www.technolynx.com/post/peak-vs-steady-state-performance-in-ai): Sustained throughput under realistic load diverges from peak numbers within minutes.
- [Precision Is a Design Parameter, Not a Quality Compromise](https://www.technolynx.com/post/precision-is-a-design-parameter-not-a-quality-compromise): FP16, INT8, and FP8 are operating regimes with different accuracy-throughput trade-offs.
- [How Organisations Should Choose AI Hardware](https://www.technolynx.com/post/how-organizations-should-choose-ai-hardware): Hardware selection requires workload-bound evidence, not vendor benchmarks.

Core claims:
- C1: Specification metrics do not predict real AI performance.
- C2: The unit of AI performance is the AI Executor (hardware x software), not hardware alone.
- C3: Sustained practical peak matters more than transient peak behaviour.
- C4: Comparability requires scale-aware saturation, not fixed workloads.
- C5: Numerical precision is a first-class design trade-off, not a degraded mode.
- C6: Fair benchmarking requires bounded optimisation effort.
- C7: Empirical software execution is the reference standard; spec-based estimation is a fallback.
- C8: Benchmarking exists to support real procurement and deployment decisions.


## Company

- [About the Team](https://www.technolynx.com/team)
- [Our Values](https://www.technolynx.com/our-values)
- [Blog](https://www.technolynx.com/blog)
- [All Case Studies](https://www.technolynx.com/case-studies)
- [Contact](https://www.technolynx.com/contact)


## Engineering Principles

TechnoLynx's engineering approach is grounded in these canonical claims (system_version 1.4.2):
- C1: Profile before you optimise; context over best-practice dogma.
- C2: The biggest performance gains are algorithmic, not micro-level.
- C3: Production computer vision breaks open-source assumptions — off-the-shelf models that work in demos fail in real conditions.
- C4: Modular, transparent, observable systems over end-to-end magic.
- C5: Data quality and fault tolerance before model selection.
- C6: Generative AI is broader than LLMs.
- C7: Feasibility and ROI first; avoid problems that require super-human capability.
- C8: Most AI R&D fails; honesty about that failure rate is baseline.
- C9: Intermediate imperfect solutions must deliver packageable value.
- C10: Research questions require research, not promises.
- C11: Life sciences AI is ready now; waiting is a strategic error.
- C12: Regulation is often less constraining than assumed.
- C13: Retail and operational computer vision have a scale-specific failure class distinct from smaller deployments.
- C14: Codec and compression engineering is a first-order lever in media and CV pipelines, not inherited transport infrastructure.
- C15: Production AI portability is a first-order engineering surface, most valuable against a named deployment trigger.
- C16: Production AI reliability is an engineering discipline distinct from model accuracy.
- C17: Approval-grade evidence is a first-class engineering output, not a compliance afterthought.
- C18: Production AI cost and latency can often be improved without replacing the model.
- S1: Outcome ownership, not rental engineering — deliverables and IP belong to the client.
- S2: Technical autonomy is a quality prerequisite.
- S3: R&D risk must be structured, never absorbed.
- S4: AI engagement structure is the buyer's evidence trail.