From pilot purgatory to production: a practical AI roadmap
Most enterprise AI projects stall after the proof of concept. We break down why pilots get stuck — and the operating model that moves them into production and measurable ROI.
Designing guardrails for autonomous AI agents
Autonomy without oversight is a liability. Here's how we scope what an agent can do, and where humans stay in the loop.
Controlling the cost of production AI
Pilots are cheap to run; production is not. The architectural choices that keep operational run cost under control without sacrificing quality.
RAG done right: grounding LLMs in your own data
Retrieval-augmented generation is easy to prototype and hard to productionize. The patterns that hold up at scale.
How to identify AI use cases worth funding
A simple scoring framework to rank candidate use cases by value, feasibility, and data readiness.
AI in regulated industries: compliance as a design input
In finance, healthcare, and government, auditability isn't a feature — it's the foundation. How we build for it.
Explainable AI: opening the black box
When AI makes a decision that affects someone, "the model said so" isn't an answer. How we build AI you can interrogate, audit, and trust.
Multi-agent systems: orchestration patterns that scale
One agent is simple. Many agents working together needs structure. The coordination patterns we rely on.
Evaluating LLM output: beyond vibes-based testing
You can't ship what you can't measure. Building evaluation suites that catch regressions before users do.
Measuring AI readiness: a maturity model that works
Before you build, know where you stand. The five dimensions we assess to gauge an organization's AI readiness.