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Design Production-Grade AI Systems
SuperML helps architects and builders implement enterprise AI architecture with depth: agents, semantic layers, NL-to-SQL, Smart SDLC, and production implementation patterns. Enterprise AI architecture guides, labs, and templates.
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Labs To Blogs: Learn, Then Run It
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Fraud Detection Lab
Real-time anomaly detection workflows with transparent model behavior.
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Agentic Fraud Lab
Rule-generation style agentic flows for modern fraud operations.
Latest From SuperML
View all posts »A curated stream of practical tutorials, production AI notes, and opinionated briefings for builders.
SR 26-2 Blew a Hole in Bank AI Governance. Now Every Model Risk Team Has to Fill It.
Federal regulators just rewrote SR 11-7 — and explicitly excluded generative AI and agentic AI from the new framework. Banks are now deploying their most powerful AI systems without a regulatory rulebook, and the internal governance vacuum is the real risk story.
OpenAI and Anthropic Adopted the Palantir Playbook. Now Enterprise Architecture Teams Need a Counter-Move.
When the two largest model labs simultaneously launched forward-deployed engineering ventures backed by Wall Street capital, they didn't just change how AI gets sold — they changed who owns your production AI architecture. Here's what that means for engineering teams trying to stay in control.
Five AI Vendors Shipped Agent Registries in One Quarter — That's Not Competition, It's a Production Crisis Signal
When Microsoft, AWS, Google, ServiceNow, and Okta all ship 'agent registries' within weeks of each other, enterprise architects need to read that convergence carefully — because the agent inventory problem is now a compliance deadline, not a backlog item.
CommBank's Fraud Agent Now Writes Its Own Detection Rules — The Architecture Shift Behind a 20% Drop in Fraud Losses
Commonwealth Bank deployed an agentic AI system that doesn't just flag fraud — it generates new detection rules in real-time, then hands them to humans for approval. The 20% fraud loss reduction is real. So is the governance architecture required to make it not a liability.
The Ontology Layer Every Enterprise AI System Needs (But Almost None Have)
Most enterprise AI systems fail in production not because the models are wrong, but because nobody defined what 'customer', 'transaction', or 'risk' means consistently across systems. This is a practical implementation guide for building the semantic layer that makes AI grounded, governed, and production-ready.
When RAG Breaks at Agent Scale: Pinecone Nexus and the Context Compilation Turn in Enterprise AI
Pinecone's pivot from vector database to 'knowledge engine' exposes a structural flaw in how enterprise teams built their RAG stacks — and signals a new architecture layer between raw data and agent runtime that will reshape how production AI systems are designed.
Cerebras Files for $26.6B IPO With OpenAI as 86% of the Backlog: The Wafer-Scale Tier Just Became an Architecture Decision
Cerebras's S-1 lands with a 750 MW OpenAI inference contract, an $1B circular loan, and 86% revenue concentration in two customers — and quietly forces enterprise AI teams to make a routing decision they've been postponing.
The NL-2-SQL Agent Trap: Why LLMs Need an Ontology Layer to Stop Hallucinating Your Database
Google's BigQuery + Gemini NL2SQL pipeline reveals the core problem every team hits: LLMs generate syntactically valid SQL that is semantically wrong. The fix isn't a better prompt — it's an ontology layer that maps business language to your actual schema. Here's the full architecture, the failure modes, and what to build.
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