Your activity metrics are up. Revenue is flat. What's wrong?
Activity metrics measure usage, not effectiveness. If you want revenue, track workflow efficiency metrics that predict retention and value.
Activity metrics measure usage, not effectiveness. If you want revenue, track workflow efficiency metrics that predict retention and value.
Most AI-augmented development workflows break when they crash into the context wall.
Every team I talk to has the same complaint: the outputs are generic. The AI sounds confident but misses what matters. Heavy editing required. Back to square one.
SR-SI fixes long-running AI context decay using a shallow index and a protocol—no embeddings, databases, or fine-tuning required.
RAG is human-curated retrieval. SR-SI is self-curated reconstruction. That shift is subtle in mechanics, but huge in implications for long-running AI work.
Oracle AI generates synthetic certainty from zero context. Embedded AI collaborates with lived project memory, maintaining continuity across sessions and building from real constraints.