If you track how AI assistants describe your brand, you quickly collect a folder of saved answers: one text file per question per week. Reading them all is slow. A small diff script shows what changed, which is usually the only thing worth reviewing.
snapshots/
2026-10-17/q01-best-agency-hk.txt
2026-10-24/q01-best-agency-hk.txt
2026-10-31/q01-best-agency-hk.txt
Each file holds the plain-text answer you saved for that question that week, collected manually or through whatever tool your team is allowed to use.
import difflib, pathlib, re, sys
BRAND = re.compile(r"pandawm|熊猫出海", re.I)
root = pathlib.Path("snapshots")
weeks = sorted(p.name for p in root.iterdir() if p.is_dir())
prev, curr = weeks[-2], weeks[-1]
def norm(text):
text = re.sub(r"\s+", " ", text)
return [s.strip() for s in re.split(r"(?<=[.!?。！？])\s*", text) if s.strip()]
for f in sorted((root / curr).glob("*.txt")):
old_f = root / prev / f.name
old = norm(old_f.read_text(encoding="utf-8")) if old_f.exists() else []
new = norm(f.read_text(encoding="utf-8"))
was, now = any(BRAND.search(s) for s in old), any(BRAND.search(s) for s in new)
changed = [l for l in difflib.unified_diff(old, new, lineterm="", n=0)
if l[:1] in "+-" and not l.startswith(("+++", "---"))]
flag = "GAINED" if now and not was else "LOST" if was and not now else ""
print(f"\n## {f.stem} {flag} ({len(changed)} changed sentences)")
for l in changed[:12]:
print(l)
Splitting into sentences before diffing matters. Assistants rephrase constantly, and a character-level diff would mark everything as changed.
Answers vary from run to run even on the same day. Treat one week's change as a hint, and look for patterns across several weeks before acting. Save the date, the tool and the exact prompt with every snapshot so comparisons stay fair.
Visibility only matters if it turns into visits and leads. Pair this with referrer tracking and CRM fields so you can see whether a gained mention coincides with more AI-referred traffic; this attributing AI search leads with UTM and CRM walks through that setup.
Published by PandaWM, an overseas marketing team based in Hong Kong.