Every resource here comes from a real engagement. Guides, templates, and checklists extracted from production AI projects — not theoretical frameworks written by people who have never shipped.
Curated paths through our resource library — from beginner to practitioner.
From tokens to transformers: understand how large language models work, how to evaluate them, and how to run them cost-effectively.
Navigate EU AI Act obligations, security red-teaming, and data governance — everything required to deploy AI in regulated environments.
From readiness to roadmap: the complete journey from assessing where you are to building and defending an AI business case.
For engineers: build production AI systems — from MCP and agentic architecture to RAG and data strategy.
High-risk AI systems must comply by August 2026. Get prepared with my complete guide and 47-point compliance checklist.
My most popular guides and templates
How manufacturers deploy Mistral AI on-premise and in air-gapped environments. Covers the Sovereign Model Ladder, Mistral Forge/Studio/Compute stack, use cases across aerospace, automotive, semiconductors, and energy, and EU AI Act compliance.
35 min read
Read NowHow to build predictive maintenance for production equipment: the data foundation (vibration, thermal, and motor-current signatures over OPC-UA and time-series), modeling approaches (anomaly detection, remaining-useful-life, survival models), edge vs cloud inference, CMMS/SCADA integration, and how to quantify ROI (downtime avoided, MTBF). Framed against ISO 13374 condition monitoring and IEC 62443 OT security. Ties to the live CSV-maintenance demo on this site.
28 min read
Read NowHow to deploy computer vision quality inspection on the production line: surface defects, assembly/completeness, and weld inspection — sensor and lighting setup, dataset and annotation strategy, and edge deployment. Includes the honesty boundary: a vision model surfaces candidate indications mapped to a vocabulary like ISO 5817 weld imperfections — it does not assign a certified grade (that needs metrology and your WPS). Ties to the live plant-audit and defect demos on this site.
26 min read
Read NowHow robotics integrators, AMR/AGV vendors, and ROS 2 teams close the sim-to-real gap: physics simulation, domain randomization, synthetic data, sim-to-real transfer, virtual commissioning, and on-robot edge inference. Covers Isaac Sim, Gazebo, MuJoCo, VLA policies, and ISO 10218 / ISO TS 15066 / IEC 61508 safety.
30 min read
Read NowDeploying AI in safety-critical embedded systems: ISO 26262 ASIL levels, SOTIF (ISO 21448), IEC 61508 SIL, IEC 62443 OT cybersecurity, runtime assurance monitors, operational design domains, and the edge inference toolchain (ONNX/TensorRT).
28 min read
Read NowA practical ROI framework for industrial digital-twin programmes: the data foundation (PLC → OPC-UA → time-series → twin), the five-rung maturity ladder, where AI enters (anomaly detection, predictive maintenance, process optimization), how to quantify ROI, and build-vs-buy guidance for manufacturing leaders.
25 min read
Read NowHow MRO operators, avionics suppliers, and UAV makers deploy AI for predictive maintenance, automated NDT, and documentation copilots — with a clear-eyed view of DO-178C / DO-254 / ARP4754A certification, EASA's learning-assurance roadmap, and the sovereign on-prem case. Civil-first; no defence contracts or clearances.
25 min read
Read NowA worked reference for production-scale Physical AI on sovereign infrastructure: an edge-deployed agentic system for EV-charging infrastructure (ISO 15118-20 / OCPP) — 400+ microservices, ~20 autonomous agents, 78% incident resolution (arXiv preprint 2603.08736). Architecture and safety proof, not a productivity claim.
20 min read
Read NowThe definitive guide to Physical AI — bridging digital intelligence with the physical world. Covers robotics, digital twins, edge computing, industrial IoT, and the 6-layer Physical AI Stack.
40 min read
Read NowWhy most AI pilots never reach production — and the proven playbook to beat those odds. Covers architecture, MLOps, monitoring, scaling, and organizational change management.
35 min read
Read NowThe definitive resource for understanding Europe's landmark AI regulation. Covers risk classification, technical requirements, penalties, and step-by-step compliance guidance. Updated for 2026.
25 min read
Read NowBuild retrieval-augmented generation systems that actually work in production. Covers architecture, chunking, embeddings, vector databases, retrieval strategies, and evaluation frameworks.
35 min read
Read NowEverything you need to build production AI agents. Covers ReAct, tool-use, and multi-agent architectures. Includes framework comparison (LangGraph, CrewAI, OpenAI Agents SDK), guardrails, evaluation, and deployment patterns.
40 min read
Read NowThe most comprehensive guide to Large Language Models: tokenization, transformer architecture, attention mechanisms, pretraining, RLHF, DPO, inference sampling, context windows, RAG, open-source models, quantization, and evaluation. 101 through expert.
60 min read
Read NowEverything about MCP: protocol architecture, transport layers (stdio, HTTP+SSE, Streamable HTTP), tools, resources, prompts, sampling, roots, security model, and full Python/TypeScript server implementation examples.
45 min read
Read NowProtect your AI systems from prompt injection, jailbreaks, data poisoning, and model theft. Covers OWASP LLM Top 10, adversarial testing methodologies, and defense-in-depth strategies for production AI.
35 min read
Read NowA practitioner template for structuring an AI safety case: the Claims–Arguments–Evidence skeleton, a HARA input sheet, ASIL/DAL/SIL decomposition placeholders, and a V&V traceability matrix — mapped to ISO 26262, DO-178C and IEC 61508. It is a template and checklist; a notified/certification body assigns the actual rating, we engineer the evidence.
Template
Read NowA vendor-neutral scoring matrix for evaluating edge-AI inference hardware and vendors: TOPS/W, P99 latency, ONNX/TensorRT-class format support, on-prem/sovereign deployment, toolchain maturity, functional-safety support, power/thermal envelope, and long-term support — with a weighted 1–5 rubric. Criteria and a scoring method, not a ranked vendor list.
Template
Read NowBuild a bulletproof AI business case. Includes cost modeling framework, ROI projections, risk quantification, stakeholder alignment templates, and a 12-month implementation timeline.
20 min read
Read NowA structured methodology for enterprise technology sourcing decisions. 6-dimension scoring framework, TCO modeling, commonalization analysis, decision trees, and governance templates — everything CTOs need to make data-driven build-vs-buy decisions.
40 min read
Read NowThe definitive compliance checklist for EU AI Act. Covers system inventory, risk classification, documentation requirements, and implementation timeline. Used by our clients to achieve audit-ready compliance.
Read NowHow manufacturers deploy Mistral AI on-premise and in air-gapped environments. Covers the Sovereign Model Ladder, Mistral Forge/Studio/Compute stack, use cases across aerospace, automotive, semiconductors, and energy, and EU AI Act compliance.
Honest side-by-side comparison of Mistral, OpenAI GPT, and Anthropic Claude for industrial and sovereign AI workloads. Covers data residency, on-prem deployment, fine-tunability, cost at scale, capability ceiling, vendor lock-in, and EU AI Act fit — plus when frontier models genuinely win.
How to build predictive maintenance for production equipment: the data foundation (vibration, thermal, and motor-current signatures over OPC-UA and time-series), modeling approaches (anomaly detection, remaining-useful-life, survival models), edge vs cloud inference, CMMS/SCADA integration, and how to quantify ROI (downtime avoided, MTBF). Framed against ISO 13374 condition monitoring and IEC 62443 OT security. Ties to the live CSV-maintenance demo on this site.
How to deploy computer vision quality inspection on the production line: surface defects, assembly/completeness, and weld inspection — sensor and lighting setup, dataset and annotation strategy, and edge deployment. Includes the honesty boundary: a vision model surfaces candidate indications mapped to a vocabulary like ISO 5817 weld imperfections — it does not assign a certified grade (that needs metrology and your WPS). Ties to the live plant-audit and defect demos on this site.
How robotics integrators, AMR/AGV vendors, and ROS 2 teams close the sim-to-real gap: physics simulation, domain randomization, synthetic data, sim-to-real transfer, virtual commissioning, and on-robot edge inference. Covers Isaac Sim, Gazebo, MuJoCo, VLA policies, and ISO 10218 / ISO TS 15066 / IEC 61508 safety.
Deploying AI in safety-critical embedded systems: ISO 26262 ASIL levels, SOTIF (ISO 21448), IEC 61508 SIL, IEC 62443 OT cybersecurity, runtime assurance monitors, operational design domains, and the edge inference toolchain (ONNX/TensorRT).
A practical ROI framework for industrial digital-twin programmes: the data foundation (PLC → OPC-UA → time-series → twin), the five-rung maturity ladder, where AI enters (anomaly detection, predictive maintenance, process optimization), how to quantify ROI, and build-vs-buy guidance for manufacturing leaders.
How MRO operators, avionics suppliers, and UAV makers deploy AI for predictive maintenance, automated NDT, and documentation copilots — with a clear-eyed view of DO-178C / DO-254 / ARP4754A certification, EASA's learning-assurance roadmap, and the sovereign on-prem case. Civil-first; no defence contracts or clearances.
A worked reference for production-scale Physical AI on sovereign infrastructure: an edge-deployed agentic system for EV-charging infrastructure (ISO 15118-20 / OCPP) — 400+ microservices, ~20 autonomous agents, 78% incident resolution (arXiv preprint 2603.08736). Architecture and safety proof, not a productivity claim.
Everything you need to build production AI agents. Covers ReAct, tool-use, and multi-agent architectures. Includes framework comparison (LangGraph, CrewAI, OpenAI Agents SDK), guardrails, evaluation, and deployment patterns.
The most comprehensive guide to Large Language Models: tokenization, transformer architecture, attention mechanisms, pretraining, RLHF, DPO, inference sampling, context windows, RAG, open-source models, quantization, and evaluation. 101 through expert.
The definitive guide to open source AI: frontier models (Llama, Mistral, Qwen, DeepSeek), training frameworks, fine-tuning with LoRA/QLoRA, inference servers (vLLM, TGI, Ollama), vector databases, orchestration frameworks, and how to choose your stack.
Complete guide to teaching AI models new skills: supervised fine-tuning (SFT), LoRA/QLoRA parameter-efficient adaptation, RLHF, DPO, GRPO, model distillation, model merging (TIES, DARE), dataset preparation, and evaluation frameworks.
Best practices for integrating Large Language Models into production systems. Covers RAG architectures, prompt engineering, evaluation frameworks, and cost optimization.
Research-backed analysis of AI ROI across industries. Includes benchmarks, success factors, and common pitfalls based on 50+ enterprise AI projects.
How leading enterprises are building AI governance frameworks that balance innovation with risk management. Includes organizational structures and policy templates.
A practitioner template for structuring an AI safety case: the Claims–Arguments–Evidence skeleton, a HARA input sheet, ASIL/DAL/SIL decomposition placeholders, and a V&V traceability matrix — mapped to ISO 26262, DO-178C and IEC 61508. It is a template and checklist; a notified/certification body assigns the actual rating, we engineer the evidence.
A vendor-neutral scoring matrix for evaluating edge-AI inference hardware and vendors: TOPS/W, P99 latency, ONNX/TensorRT-class format support, on-prem/sovereign deployment, toolchain maturity, functional-safety support, power/thermal envelope, and long-term support — with a weighted 1–5 rubric. Criteria and a scoring method, not a ranked vendor list.
Kick off your AI project right with this comprehensive charter template. Includes sections for objectives, success metrics, risks, and resource requirements.
Evaluate your organization's AI readiness across data, technology, people, and governance dimensions.
From GPT wrapper to investable AI platform — an illustrative 90-day architecture transformation covering technical-debt elimination, SOC 2 readiness, and investor narrative. Illustrative scenario; any outcome depends on the client's baseline, scope and implementation conditions.
These guides cover the common patterns. Your situation has specific constraints — industry, tech stack, team, timeline. A 30-minute call is enough to map out what applies to you.