Applied AI
Relational RAG: joining SQL attributes with embeddings
Relational RAG unifies structured SQL attributes with embeddings to deliver precise, scalable retrieval in production AI.
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Technical Advisory & Systems Research
Engineering Perspectives
Deep dives into Agentic Workflows, distributed systems, and the architectural rigor required to move AI from experimentation to enterprise-grade production.
Applied AI
Rendering responsive image variations for production AI systems
In production AI systems, delivering the right image variant for each device profile is a business constraint as much as a technical requirement.
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Applied AI
Replicating Production Anomalies in Mock Environments: A Skills-Driven Guide for Safe AI Pipelines
In modern AI production, data anomalies and drift can derail models and erode trust. The cost of catching these issues in live systems is high, and rollbacks after incidents are disruptive to business operations. Building resilient pipelines requires testing with realistic yet controlled data in isolated environments.
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Applied AI
Reranking at scale with CLAUDE.md templates
In production AI, reranking is not optional; it is the primary mechanism that preserves relevance when data scales and latency budgets tighten.
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Applied AI
Reusable AI skills for dynamic UI skeletons in streaming interfaces
In modern streaming UIs and dashboards, content arrives in waves. Layout stability matters for user trust and conversion.
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Architecture
Roadmap for Migrating Legacy Sync Logic to Non-Blocking Async Loops
Modern production AI systems demand non-blocking I/O to meet latency targets under heavy load. Synchronous patterns block threads and waste resources when data must be fetched from databases, vector stores, or external services.
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Applied AI
Robust integration tests for complex financial calculations without live payment layers
Financial calculations in production systems touch real money, and mistakes ripple through revenue, compliance, and customer trust.
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Applied AI
Robust rollback strategies for partial data writes in multi-agent AI pipelines
Partial data writes across iterative AI agent loops can leave system state inconsistent and complicate recovery. In practice, production-grade AI pipelines require predictable rollback paths, guardrails, and reusable templates that engineering teams can trust.
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Applied AI
Robust schema testing with ephemeral containerized data environments
In production AI pipelines, schema correctness is a systemic risk that scales with data velocity and model reuse. The cost of undetected schema drift compounds quickly through downstream features, dashboards, and governance gates.
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