Practitioner-level writing on ERP modernisation, data architecture, AI/ML systems, and autonomous agents — from engineers who build these systems in production.
Latest Articles
Most ERP vendors are adding AI as a feature layer on top of 20-year-old data models. Here's why that approach fails — and what a genuinely AI-native ERP modernisation looks like in practice.
Hallucinated case law is a catastrophic failure mode for any legal AI. Here's the verification architecture behind Mr.Lawyer — how every act, section and judgment is checked before it reaches the user.
Data warehouses are too rigid. Data lakes are too unstructured. The lakehouse architecture combines the best of both — and it's the foundation every modern AI system needs. Here's how we build them.
Most multi-agent demos look impressive and fail in production. Here's how we architect LangGraph-based systems for real enterprise workloads — state management, error recovery, human-in-the-loop, and monitoring.
No-shows cost clinics 15–20% of revenue. ClinicAssist's multi-step reminder and re-engagement system fills freed slots automatically. Here's how the agent logic works in production.
Manufacturing operations run on data that's often 24–48 hours stale. Here's how we replaced overnight batch jobs with real-time streaming pipelines — and what changed when plant managers could see yesterday's data today.
Azure is our primary cloud for enterprise AI workloads. Here's the reference architecture we've refined across multiple deployments — AKS, Azure OpenAI, Databricks, and the networking patterns that keep it secure.
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Every article here is drawn from real production experience. If you're building something similar, we're happy to have a technical conversation — no sales pitch.
