Blog
Practical perspectives on enterprise AI — from ROI frameworks to production architecture patterns.
Evaluate potential AI initiatives through operating costs, workflow quality, and implementation tradeoffs, using documented assumptions rather than promised returns.
Evaluation Framework
Costs, workflow quality, and implementation tradeoffs
Compare retrieval and model adaptation using representative data, source freshness, evaluation quality, latency constraints, and total operating cost.
Explore task separation, orchestration, human review, and failure handling when evaluating a multi-agent design.
Use practical questions about data, technical ownership, sponsorship, use cases, and change management to identify planning needs.
Tell us what you’re trying to solve. We’ll discuss the workflow, constraints, and possible next steps.