Stop stuffing context. Build it.
Most teams build context windows by concatenating files until they’re close to the model’s limit. The result is bloated, redundant, and randomly ordered. Our context builder treats the context window as a budgeting problem: pack the most relevant, least redundant material first, in the order the model reads best, with clear section delimiters.
You select source documents (Markdown, text, URLs, transcripts), pick a target model and budget, and the tool produces a single Markdown payload — ready to paste or send via API — that maximizes the signal per token.
What it does
- Token-aware packing for any major model
- Deduplication of repeated paragraphs across sources
- Optional ordering by relevance to a query
- Section delimiters and source tags so the model can cite
- Compression of low-signal sections via summarization (optional)
- Reproducible — same inputs and settings produce the same output
The output is a clean Markdown document plus a metadata block listing every source and how many tokens it consumed.