ReclaimLLM case studies
Evidence, workflows, and the limits of the claim.
Technical studies of how captured AI work can reduce context waste and preserve the reasoning behind a codebase.
Measured results stay separate from illustrative workflows. Every study states what the evidence supports and what it does not.
Study index
Choose the question.
Token capacity from tool-result reduction
A bounded shadow-mode replay of captured coding sessions, with the eligibility funnel, methodology, limitations, and defensible result kept visible.
16.46%
reduction in replayed text tool-result tokens
→Turning AI session history into codebase memory
How searchable sessions, file history, source inspection, and focused summaries can help a developer recover the reasoning behind unfamiliar code.
Search → inspect → reuse
a source-backed recall workflow
→Evidence standard
A useful case study makes uncertainty visible.
ReclaimLLM distinguishes measured observations from projections, examples, and unsupported conclusions. The goal is evidence an engineering team can inspect—not a marketing number it has to trust.