Before
Large context windows created high input-token cost and inconsistent source quality.
Illustrative case study
Enterprise RAG assistant: fewer chunks, stronger evidence.
An enterprise knowledge assistant retrieved many chunks for every request, including stale or low-trust content that increased cost and noise.
4waste sources mapped
3safe controls proposed
1audit-ready business case
ML Mind analysis
Scored chunks by relevance, trust, freshness and citation value.
Controls applied
Reduced context to the smallest trusted evidence set and preserved protected facts.
Outcome
Lower token load, clearer citations and safer answers for internal users.
This is an illustrative scenario for product education. Real savings should be validated using customer telemetry, deployment level, provider pricing and answer integrity checks.
Turn this page into a validated savings map.
Use ML Mind to identify where AI spend is leaking, which controls are safe at your deployment level, and what evidence your team needs for an audit, pilot or executive review.