Engineering Intelligence
Perspectives, research, and technical deep-dives from the Cognisi Labs engineering team.
Building Production-Grade Agentic AI Systems: Architecture Patterns and Pitfalls
Agentic AI systems that can plan, reason, and execute multi-step tasks are moving from research labs to enterprise production. Here's what we've learned building them at scale.
RAG vs. Fine-Tuning: Choosing the Right LLM Adaptation Strategy for Enterprise
When should you fine-tune a model versus building a RAG pipeline? We break down the trade-offs across accuracy, cost, latency, and maintainability.
UVM Verification Strategies for AI Accelerator Chips: Lessons from the Field
Verifying AI accelerator chips presents unique challenges — massive parallelism, complex memory hierarchies, and tight power budgets. Here's our verification methodology.
Zero Trust Architecture Implementation: A Practical Enterprise Guide
Zero trust is more than a buzzword — it's a fundamental shift in how enterprises secure their infrastructure. Here's a practical implementation roadmap.
Platform Engineering: Building Internal Developer Platforms That Actually Get Adopted
Most IDP initiatives fail not because of technology, but because of adoption. We share the patterns that make developer platforms successful in large enterprises.
Evaluating LLM Output Quality: Beyond BLEU Scores and Human Evaluation
Robust LLM evaluation is one of the hardest problems in enterprise AI. We explore automated evaluation frameworks that scale with your deployment.
Composable Architecture: Why Enterprises Are Moving Beyond Monolithic Platforms
Composable architecture enables enterprises to build modular, flexible technology stacks. Here's how to make the transition without disrupting operations.
Multi-Agent Orchestration: Designing Reliable AI Workflows for Enterprise Operations
Multi-agent systems unlock powerful automation capabilities, but designing them for reliability and observability in enterprise environments requires careful architecture.
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