The proof, not the pitch — measured, not estimated

1 · Token reduction at scale — 321 repositories

1,765,696,549 → 23,427,118 tokens — an 98.7% overall reduction (95.6% average per repo). 84 of 405repos use languages SigMap doesn't yet parse and are excluded from the headline.

2 · Real coding tasks — 51 tasks, with vs without SigMap

For each task we measured the tokens an LLM needs to answer using the whole repo versus only the files SigMap ranks. Tokens are model-reported; cost is derived.

3 · Does it also make a real agent faster? — we tested it honestly

The savings above are about context size and cost, and they're deterministic. A separate question is whether smaller context also makes an autonomous agent finish faster. We A/B-tested one (Devin) on these tasks — A = task only, B = SigMap context first — at 3 reps each. The honest result: too close to call.

What this means: this is notevidence SigMap slows an agent down — the two are statistically even. The agent's run-to-run variance is simply large (some runs exceeded our 30-min measurement cap), so we can't yet claim a speedup either way. An early single run looked like a big win (~61%) but didn't hold up across 3reps — so we're showing you the real result, not that number. SigMap's proven value is the ~99% smaller, 96× cheaper, better-retrieved context above— what your agent does with that head start is the open question we're still measuring.