Thinking from the AI frontline.
Deep technical articles, honest post-mortems, and practitioner guides from our team of 500+ data scientists and engineers.
Why 70% of Enterprise RAG Systems Fail in Production (And How to Fix Yours)
After building 40+ enterprise RAG systems, we've identified the seven failure patterns that kill LLM applications before they deliver value. Here's the definitive playbook for building RAG that actually works.
The MLOps Maturity Model: Where Are You and Where Should You Be?
A practical framework for assessing your organisation's ML deployment maturity — from ad-hoc notebooks to fully automated production pipelines.
dbt Semantic Layer vs. Looker LookML: A 2026 Decision Framework
Both solve the 'single source of truth' problem — but they make different trade-offs. Here's how we decide which to recommend for our clients.
Real-time Fraud Detection at 2M Transactions Per Second: Architecture Deep Dive
The full technical architecture behind the fraud system that saved our banking client $240M annually — including the parts that failed in staging.
Measuring and Mitigating Bias in Credit Scoring Models
A practical guide to fairness testing, disparate impact analysis, and bias mitigation techniques that actually hold up under regulatory scrutiny.
The Hidden Cost of Your Data Warehouse: A FinOps Framework for Snowflake and BigQuery
Enterprise data warehouses regularly run 3–5× more expensive than they need to. Here are the optimisation patterns we apply on every engagement.
Fine-tuning vs RAG: The Decision Framework Our Team Uses on Every GenAI Project
The answer isn't always RAG. This framework helps you decide when fine-tuning beats retrieval — and when it's not worth the cost.
Building Clinical AI That Clinicians Actually Use: Lessons from 52 Hospitals
Technical performance doesn't drive adoption — trust does. Here's how we design clinical AI workflows that physicians embrace rather than ignore.
Why We Stopped Using XGBoost for Demand Forecasting (And What We Use Instead)
After 10 years of gradient boosted trees, temporal fusion transformers and N-BEATS have fundamentally changed what's possible in demand sensing.
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