fix: knowledge retrieval accuracy and remaining brand/PoC leaks in procedure docs

- Fix the demo knowledge provider's tokenizer: a plain [a-z0-9]+ regex silently
  dropped accented characters, splitting French words like "véhicule" into "v" +
  "hicule" and mangling retrieval for nearly every French query. Now matches the
  Latin-1 accented range too.
- Reweight section scoring so the body match (the actual substance of a section)
  outranks a heading/title match (a shallow structural hint) rather than the reverse
  -- confirmed via the brief's exact validation question that the old weighting
  misranked the damage procedure behind a topically-adjacent document in all three
  languages (nl-BE: a checkout section; en-GB/fr-BE: the return procedure), purely
  because a generic word like "vehicle"/"voertuig" happened to sit in a heading/title.
- Remove leftover "MobilityOps" and "PoC" mentions from 5 English and 4 NL/FR
  procedure documents -- knowledge-base prose is visible UI content and was missed by
  the earlier rebrand.
- Add regression tests: the brief's exact NL/EN/FR damage question must ground on the
  damage procedure as the *primary* source (not just appear in the top 3), and no
  procedure file may contain "MobilityOps" or "PoC".

151 backend tests, Ruff, mypy green.

Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>

backend/app/services/knowledge/demo.py

@@ -35,7 +35,11 @@ STOPWORDS_BY_LANGUAGE: dict[str, set[str]] = {
     },
 }
 
-_WORD_RE = re.compile(r"[a-z0-9]+")
+# Includes the Latin-1 accented-letter range (à-ö, ø-ÿ) so French/Dutch words with
+# diacritics (véhicule, réservation, geëscaleerd) tokenize as one word instead of
+# splitting apart at the accented character -- a plain [a-z0-9]+ pattern silently
+# drops every accent and fragments the word either side of it.
+_WORD_RE = re.compile(r"[a-zà-öø-ÿ0-9]+")
 
 
 def _stem(word: str) -> str:
@@ -218,17 +222,28 @@ class DemoKnowledgeProvider:
     def _score(
         self, query_tokens: set[str], section: ScoredSection, idf: dict[str, float]
     ) -> float:
+        # The section body is the strongest relevance signal -- it's the actual
+        # substance a heading or title can only hint at -- so a body match is weighted
+        # *above* heading/title matches, not below them. The previous 3x/2x/1x
+        # (heading/title/body) ordering let a single generic word in a heading (e.g.
+        # "vehicle", present in nearly every section) or a document's own title
+        # outrank a section whose body genuinely covers multiple, more distinctive
+        # query terms -- confirmed to misrank the brief's exact validation question in
+        # every one of the three languages (see docs/fleet-ops-correction/
+        # current-gap-audit.md and i18n-inventory.md): nl-BE picked a checkout section
+        # over the damage procedure, en-GB and fr-BE picked the return procedure over
+        # the damage procedure, purely from heading/title overlap on common words.
         score = 0.0
         for token in query_tokens:
             token_idf = idf.get(token, 0.0)
             if token_idf == 0.0:
                 continue
-            if token in section.heading_tokens:
+            if token in section.body_tokens:
                 score += 3 * token_idf
-            elif token in section.document.title_tokens:
+            elif token in section.heading_tokens:
                 score += 2 * token_idf
-            elif token in section.body_tokens:
-                score += token_idf
+            elif token in section.document.title_tokens:
+                score += 1.5 * token_idf
         return score
 
     def ask(