Insights
Product, design, AI, and engineering perspectives from our team.
Custom AI Agent Development: Architecture and Use Cases
AI agents are autonomous software systems that plan, use tools, and execute multi-step tasks — a significant step beyond standard LLM calls. This guide covers the core architectural patterns, industry use cases, data prerequisites, and what a responsible commissioning process looks like for teams considering custom AI agent development.
Context Engineering for LLM Apps: Beyond Prompt Templates
Prompt templates are where LLM applications start. Context engineering is what makes them work reliably in production. This article covers the four core levers — retrieval, compression, memory, and ordering — and how to build a context pipeline that produces consistent, cost-efficient model behaviour at scale.
AI Incident Response: What to Do When Your Model Fails in Production
When an AI model fails in production, the failure is often silent — no error code, just degrading outputs. This guide is a practical incident response playbook for ML and LLM systems: detection, severity classification, rollback, stakeholder communication, and post-incident review, built for technical leaders who need to extend their existing incident processes to cover AI-specific failure modes.
Fine-Tuning Small Language Models for Domain-Specific Tasks
Fine-tuning a small language model can outperform a frontier model on narrow tasks — but only when the task, data, and economics actually justify the overhead. This article covers when fine-tuning makes sense, how to prepare data and evaluate properly, and how to honestly assess the cost trade-off against prompting a frontier model API.
Caching Strategies for LLM Applications: Reducing Latency and Cost
Caching is one of the most underused levers for reducing cost and latency in production LLM applications. This article covers prompt caching, semantic caching, and response caching — what each layer does, when to use it, and how to think about invalidation and observability.
Structured Outputs and Function Calling: Making LLMs Reliable
Structured outputs and function calling are the mechanisms that make LLMs viable in production workflows — but reliable implementation requires deliberate schema design, validation layers, and observability from the start. This guide covers how to use both patterns effectively, when to choose each, and the failure modes that catch teams off-guard at scale.
Agentic RAG: When Retrieval Needs Reasoning
Standard RAG works well when one retrieval pass is enough. Agentic RAG is the architecture for problems that require planning, iterative retrieval, and reasoning over results from multiple sources. This post covers the patterns, the platform options, and the real engineering trade-offs.
Building AI Products on Supabase, PostgreSQL and pgvector
For lean engineering teams building AI features, Supabase and PostgreSQL have become a serious default stack. This article covers why the combination works, where pgvector fits into RAG architectures, how row-level security keeps AI features production-safe, and what the trade-offs look like as you scale.
MLflow vs Weights & Biases: Experiment Tracking and Model Registry
MLflow and Weights & Biases are the two platforms most growing ML teams evaluate for experiment tracking and model registry. This guide compares them honestly across deployment model, data residency, collaboration, and production reproducibility — so you can make the right call for your team and regulatory context.