Stable by design
More data should not change the core logic. Scale can increase storage and compute pressure, but the evidence model and ranking rules should stay clear.
Project direction
Designing ranking strategies for unstructured data.
The goal is simple: keep the ranking architecture stable as data grows. New unstructured data is mapped to a defined knowledge base, then improved over time.
Math before generation
The system has a defined boundary for what it can rank and connect. LLMs help link unknown data to that boundary, but they do not replace it.
Project loop
Math defines the boundary. Feedback improves the system.
Math boundary
Define what the system can rank, connect, and verify before the first hard data pass.
Link unknowns
When unexpected data appears and the knowledge base is stale, LLMs map it to known concepts.
Relevant results
Generate and rank the most relevant results from the current knowledge base.
Human choice
The user chooses the best results. That choice becomes reinforcement for the next loop.
Stable scale
The knowledge base improves, while the architecture stays defined as data grows.
The core concepts will be released once planned-scale validation is complete.