Applied AI
Designing production-grade AI systems for enterprise marketing automation
Enterprise marketing teams need reliable, auditable AI that can be deployed quickly and governed rigorously.
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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
Designing Reliable AI for Complex Tasks in Production
Yes, AI can handle complex tasks in production when embedded in disciplined agentic workflows, governed by data contracts, and operated within robust distributed architectures.
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Applied AI
Designing robust human evaluation UI for enterprise AI
A robust human evaluation UI is essential for reliable, production-grade AI. It standardizes feedback, preserves provenance, and provides auditable traces that strengthen governance and compliance in enterprise deployments.
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Technical Advisory
Designing Robust Multi-Agent Teams for Complex Workflows
Multi-Agent Orchestration is not a theoretical abstraction. It is a production-grade approach to building teams of specialized agents that operate across a distributed tech stack with explicit interfaces, deterministic state, and principled governance.
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Technical Advisory
Designing Token Economies for Scalable Agent Swarms in Production AI
Token-based control is not marketing fluff—it's a production discipline that stabilizes extreme-volume inference by coordinating compute, data, and priority across thousands of autonomous actors.
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Applied AI
Designing Tone-Aware Agents for High-Stress Support
Designing tone-aware agents for high-stress support requires more than clever prompts. It demands disciplined architecture where tone control is policy-driven, auditable, and resilient under load.
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Applied AI
Detecting and Blocking Indirect Prompt Injection in Client-Facing RAG: Architecture and Best Practices
Indirect prompt injection in client-facing retrieval-augmented generation (RAG) pipelines is a real production threat. It arises not from a visible prompt, but from contaminated data, memory, and tool signals that subtly steer model behavior.
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Applied AI
Detecting and Containing AI Errors in Production
Detecting AI errors in production is not about chasing edge cases; it is about building a reliable AI fabric that can be observed, measured, and controlled.
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Applied AI
Detecting anomalies in marketing data with AI agents before reporting
Marketing data environments are increasingly complex, with signals streaming from ad platforms, CRM systems, attribution models, and website analytics.
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