Latest Insight
Writing from the people who run our delivery teams — on agentic AI, LLM applications, quality, and scaling engineering capacity.
August 15, 2026 · 7 min read
Automate Operations by Targeting Exceptions, Not Tasks
A practical architecture for using AI to reduce exception queues without surrendering operational control.
August 13, 2026 · 7 min read
Measure AI Coding Gains at the Pull Request Level
AI assistants improve delivery only when teams measure flow, review load, rework, and production outcomes—not generated code.
August 11, 2026 · 7 min read
Secure Internal AI Copilots With a Data Access Gateway
A practical architecture for giving AI copilots useful company context without bypassing permissions, ownership, or audit controls.
August 9, 2026 · 7 min read
How to Build a Test Suite for LLM-Powered Features
A practical QA framework for testing LLM behavior, tool use, safety, and regressions before an AI feature reaches production.
August 7, 2026 · 7 min read
Treat AI Quality as a Production Control Loop
Connect traces, targeted evaluations, and release controls so AI failures become measurable engineering work rather than user anecdotes.
August 3, 2026 · 7 min read
Design RAG Around Evidence Lifecycles, Not Vector Search
Reliable RAG depends on how evidence is created, governed, assembled, cited, and retired—not on the choice of vector database.
August 1, 2026 · 7 min read
How Nearshore Teams Should Work With AI Agents
A practical operating model for combining nearshore engineering capacity with agents without weakening ownership, security, or delivery quality.
July 31, 2026 · 7 min read
Build Internal AI Copilots Around Decisions, Not Documents
A practical operating model for turning company knowledge into useful copilots without weakening permissions, ownership, or trust.
July 29, 2026 · 7 min read
AI Code Assistants Speed Typing, Not Delivery by Default
AI coding tools can shorten implementation work, but delivery improves only when teams control review load, rework, testing, and batch size.
July 28, 2026 · 7 min read
How to Observe and Evaluate AI Features in Production
A practical operating model for tracing AI requests, measuring quality, detecting regressions, and turning production failures into better evaluations.
July 28, 2026 · 7 min read
A Production Architecture for Retrieval-Augmented LLM Apps
A practical architecture for building RAG systems that stay observable, testable, secure, and useful as data and traffic grow.
July 28, 2026 · 7 min read
Fine-Tuning, RAG, or Prompting: A Practical Decision Guide
Choose the least complex LLM adaptation method that meets your requirements for knowledge, behavior, latency, cost, and governance.
July 28, 2026 · 7 min read
Choosing Multi-Agent Patterns for Reliable Tool Use
A practical guide to routing, delegation, handoffs, and safe tool execution in production multi-agent systems.
July 28, 2026 · 7 min read
Reliable Multi-Agent Systems Need Explicit Control
A practical guide to routing, delegation, tool execution, and failure handling in production multi-agent systems.
July 28, 2026 · 7 min read
Prompt Engineering Is Not Enough for Production AI
Reliable AI systems depend less on clever prompts than on disciplined control of context, tools, state, evaluation, and failure modes.