Input normalizer + unified memory query — "seamless with whatever input"

J asked directly: "did we implement our memory findings so that our
knowledge base and our configuration playbook [work] seamlessly with
whatever input they're given?" Honest answer tonight was "one of five
findings shipped, normalizer is the blocker." This closes that gap.

NORMALIZER (tests/multi-agent/normalize.ts):
Accepts structured JSON, natural language, or mixed. Returns canonical
NormalizedInput { role, city, state, count, client, deadline, intent,
confidence, extraction_method, missing_fields } for any downstream
consumer.

Three-tier path:
  1. Structured fast-path — already-shaped input skips LLM
  2. Regex path — "need 3 welders in Nashville, TN" parses without LLM.
     City/state parser tightened to 1-3 capitalized words + "in {city}"
     anchor preference + case-exact full-state-name variants to prevent
     "Forklift Operators in Chicago" being captured as the city name
  3. LLM fallback — qwen3 local with think:false + 400 max_tokens for
     inputs the regex can't handle

Unit tests (tests/multi-agent/normalize.test.ts): 9/9 pass. Covers
structured fast-path, misplacement→rescue intent, state-name→abbrev
conversion, regex extraction from natural language, plural role +
full state name edge case, rescue intent keyword precedence, partial
input reporting missing fields, empty object fallthrough, async/sync
parity on clean inputs.

UNIFIED MEMORY QUERY (tests/multi-agent/memory_query.ts):
One function, five parallel fan-outs, one bundle returned:
  - playbook_workers — hybrid_search via gateway with use_playbook_memory
  - pathway_recommendation — KB recommender for this sig
  - neighbor_signatures — K-NN sigs weighted by staffer competence
  - prior_lessons — T3 overseer lessons filtered by city/state
  - top_staffers — competence-sorted leaderboard
  - discovered_patterns — top workers endorsed across past playbooks
    for this (role, city, state)
  - latency_ms — per-source + total
Every branch is best-effort: one source down doesn't break the bundle.

HTTP ENDPOINT (mcp-server/index.ts):
  POST /memory/query with body {input: <anything>} → MemoryQueryResult
Returns the same shape the TS function does. Typed with types.ts for
future UI consumption.

VERIFIED:
  curl POST /memory/query with structured {role,city,state,count}
    → extraction_method=structured, 10 playbook workers, top score 0.878
  curl POST /memory/query with "I need 3 welders in Nashville, TN"
    → extraction_method=regex (no LLM call), 319ms total, 8 endorsements
      for Lauren Gomez auto-discovered as top Nashville Welder

Honest remaining gaps (documented for next phase):
  - Mem0 ADD/UPDATE/DELETE/NOOP — we still only ADD + mark_failed
  - Zep validity windows — playbook entries have timestamps but no
    retirement semantic
  - Letta working-memory / hot cache — every query scans all 1560
    playbook entries
  - Memory profiles / scoped queries — global pool, no per-staffer
    private subsets

2 of 5 findings now shipped (multi-strategy retrieval in Rust, input
normalization + unified query in TS). The remaining 3 are architectural
additions queued as Phase 25 items — validity windows first since it's
the most load-bearing for long-running systems.

mcp-server/index.ts

@@ -789,6 +789,26 @@ async function main() {
         // workers_500k manifest is correct (500K); candidates manifest
         // lied (said 100K, actual 1K) — the audit caught it.
         // Everything else uses manifest row_count since it's O(1).
+        // Phase 24 refinement — unified memory query endpoint. Accepts
+        // any input (natural language, structured JSON, mixed) via
+        // POST body {input: <anything>}. Normalizer handles the shape.
+        // Returns a single bundle with every memory surface relevant:
+        // playbook workers, KB recommendation, neighbor signatures,
+        // prior lessons, top staffers, discovered patterns.
+        if (url.pathname === "/memory/query" && req.method === "POST") {
+          try {
+            const body: any = await req.json();
+            const { queryMemory } = await import("../tests/multi-agent/memory_query.ts");
+            const result = await queryMemory(body.input ?? body);
+            return ok(result);
+          } catch (e) {
+            return new Response(JSON.stringify({ error: (e as Error).message }), {
+              status: 500,
+              headers: { "content-type": "application/json" },
+            });
+          }
+        }
+
         if (url.pathname === "/system/summary") {
           const [ds, indexes, workersCount, candsCount] = await Promise.all([
             api("GET", "/catalog/datasets").catch(() => [] as any),

tests/multi-agent/memory_query.ts

@@ -0,0 +1,239 @@
+// Unified memory query — one surface that takes any input, normalizes
+// it, and returns every memory signal the system has: playbook workers,
+// KB pathway recommendations, prior lessons, staffer competence stats,
+// and cross-staffer discovered patterns. This is the "seamlessly with
+// whatever input" answer J framed it as.
+//
+// Why a unified gateway instead of separate calls: the memory surfaces
+// are semantically related (playbook_memory workers reference runs the
+// KB tracks; prior_lessons reference staffers the stats rank). Callers
+// shouldn't have to know the topology.
+
+import { readFile, readdir } from "node:fs/promises";
+import { join } from "node:path";
+import { normalizeInput, type NormalizedInput } from "./normalize.ts";
+import { findNeighbors, loadRecommendation, loadStafferStats, computeSignature, type StafferStats, type PathwayRecommendation } from "./kb.ts";
+import { GATEWAY, SIDECAR } from "./agent.ts";
+
+export interface MemoryQueryResult {
+  input: NormalizedInput;
+  playbook_workers: Array<{
+    doc_id: string;
+    name: string;
+    score: number;
+    playbook_boost: number;
+    playbook_citations: string[];
+  }>;
+  pathway_recommendation: PathwayRecommendation | null;
+  neighbor_signatures: Array<{
+    sig_hash: string;
+    events_digest: string;
+    similarity: number;
+    weighted_score: number;
+    best_staffer_id: string | null;
+  }>;
+  prior_lessons: Array<{
+    date: string;
+    client: string;
+    cities: string;
+    lesson: string;
+  }>;
+  top_staffers: StafferStats[];
+  discovered_patterns: {
+    sig_hash: string;
+    top_workers: Array<{ name: string; endorsements: number }>;
+  } | null;
+  latency_ms: {
+    normalize: number;
+    playbook_search: number;
+    kb_neighbors: number;
+    staffer_stats: number;
+    prior_lessons: number;
+    total: number;
+  };
+}
+
+// Main entry. Normalizes the input, then fans out to every memory
+// source in parallel. Each source is best-effort: a down dependency
+// just leaves its field empty rather than breaking the query.
+export async function queryMemory(raw: unknown): Promise<MemoryQueryResult> {
+  const t0 = Date.now();
+
+  // Normalize input first — sets the tone for every downstream call.
+  const tNorm = Date.now();
+  const input = await normalizeInput(raw);
+  const normalize_ms = Date.now() - tNorm;
+
+  // Synthesize a minimal scenario spec from normalized input for sig
+  // computation + KB lookup. Only needed if we have at least role + city.
+  const pseudoSpec = (input.role && input.city && input.state) ? {
+    client: input.client ?? "(unknown)",
+    events: [{
+      kind: input.intent === "rescue" ? "misplacement" : "baseline_fill",
+      role: input.role,
+      count: input.count ?? 1,
+      city: input.city,
+      state: input.state,
+    }],
+  } : null;
+
+  // Fire everything that CAN fire in parallel.
+  const tPb = Date.now();
+  const playbookPromise: Promise<MemoryQueryResult["playbook_workers"]> =
+    (input.role && input.city && input.state)
+      ? fetch(`${GATEWAY}/vectors/hybrid`, {
+          method: "POST",
+          headers: { "content-type": "application/json" },
+          body: JSON.stringify({
+            index_name: "workers_500k_v1",
+            sql_filter: `role = '${input.role.replace(/'/g, "''")}' AND city = '${input.city.replace(/'/g, "''")}' AND state = '${input.state}' AND CAST(availability AS DOUBLE) > 0.5`,
+            question: `${input.role} ${input.city} ${input.state}`,
+            top_k: 10,
+            generate: false,
+            use_playbook_memory: true,
+            playbook_memory_k: 100,
+          }),
+        }).then(r => r.ok ? r.json() : { sources: [] })
+          .then((d: any) => (d.sources ?? []).map((s: any) => ({
+            doc_id: s.doc_id,
+            name: s.chunk_text?.split("—")[0]?.trim() ?? "?",
+            score: s.score ?? 0,
+            playbook_boost: s.playbook_boost ?? 0,
+            playbook_citations: s.playbook_citations ?? [],
+          })))
+          .catch(() => [])
+      : Promise.resolve([]);
+
+  const tKb = Date.now();
+  const recPromise = pseudoSpec
+    ? loadRecommendation(pseudoSpec).catch(() => null)
+    : Promise.resolve(null);
+
+  const neighborsPromise = pseudoSpec
+    ? findNeighbors(pseudoSpec, 5).catch(() => [])
+    : Promise.resolve([]);
+
+  const tLess = Date.now();
+  const lessonsPromise: Promise<MemoryQueryResult["prior_lessons"]> =
+    (input.city && input.state)
+      ? loadRelevantLessons(input.city, input.state).catch(() => [])
+      : Promise.resolve([]);
+
+  const tStaff = Date.now();
+  const staffersPromise = loadStafferStats().catch(() => []);
+
+  // Parallel await
+  const [playbook_workers, pathway_recommendation, neighbors_raw, prior_lessons, staffers] =
+    await Promise.all([playbookPromise, recPromise, neighborsPromise, lessonsPromise, staffersPromise]);
+
+  // Derived: top-k staffers by competence, from recent activity.
+  const top_staffers = staffers
+    .filter(s => s.total_runs > 0)
+    .sort((a, b) => b.competence_score - a.competence_score)
+    .slice(0, 5);
+
+  // Derived: discovered patterns for this sig (workers endorsed ≥ 2 staffers).
+  let discovered_patterns: MemoryQueryResult["discovered_patterns"] = null;
+  if (pseudoSpec) {
+    const sig_hash = computeSignature(pseudoSpec);
+    const workers = await collectTopEndorsedWorkers(sig_hash, input.role, input.city, input.state).catch(() => []);
+    if (workers.length > 0) discovered_patterns = { sig_hash, top_workers: workers };
+  }
+
+  return {
+    input,
+    playbook_workers,
+    pathway_recommendation,
+    neighbor_signatures: neighbors_raw.map(n => ({
+      sig_hash: n.sig.sig_hash,
+      events_digest: n.sig.events_digest,
+      similarity: n.similarity,
+      weighted_score: n.weighted_score,
+      best_staffer_id: n.best_staffer_id,
+    })),
+    prior_lessons,
+    top_staffers,
+    discovered_patterns,
+    latency_ms: {
+      normalize: normalize_ms,
+      playbook_search: Date.now() - tPb,
+      kb_neighbors: Date.now() - tKb,
+      staffer_stats: Date.now() - tStaff,
+      prior_lessons: Date.now() - tLess,
+      total: Date.now() - t0,
+    },
+  };
+}
+
+async function loadRelevantLessons(city: string, state: string | null): Promise<MemoryQueryResult["prior_lessons"]> {
+  try {
+    const dir = join("data", "_playbook_lessons");
+    const files = await readdir(dir).catch(() => [] as string[]);
+    const out: MemoryQueryResult["prior_lessons"] = [];
+    for (const f of files) {
+      if (!f.endsWith(".json")) continue;
+      try {
+        const raw = await readFile(join(dir, f), "utf8");
+        const rec = JSON.parse(raw);
+        const lessonCities = (rec.cities ?? "").split(",");
+        const lessonStates = (rec.states ?? "").split(",");
+        if (lessonCities.includes(city) || (state && lessonStates.includes(state))) {
+          out.push({
+            date: rec.date,
+            client: rec.client,
+            cities: rec.cities,
+            lesson: rec.lesson.slice(0, 500),
+          });
+        }
+      } catch { /* skip malformed */ }
+    }
+    out.sort((a, b) => (b.date ?? "").localeCompare(a.date ?? ""));
+    return out.slice(0, 3);
+  } catch {
+    return [];
+  }
+}
+
+async function collectTopEndorsedWorkers(
+  sig_hash: string,
+  role: string | null,
+  city: string | null,
+  state: string | null,
+): Promise<Array<{ name: string; endorsements: number }>> {
+  if (!role || !city || !state) return [];
+  // Pull from playbook_memory — each successful fill was endorsed;
+  // count by (name, role, city, state) across past playbooks.
+  try {
+    const resp = await fetch(`${GATEWAY}/query/sql`, {
+      method: "POST",
+      headers: { "content-type": "application/json" },
+      body: JSON.stringify({
+        sql: `SELECT operation, result FROM successful_playbooks_live
+              WHERE operation LIKE 'fill: ${role.replace(/'/g, "''")} %'
+                AND operation LIKE '%in ${city.replace(/'/g, "''")}, ${state}%'
+              ORDER BY timestamp DESC LIMIT 50`,
+      }),
+    });
+    if (!resp.ok) return [];
+    const data: any = await resp.json();
+    // successful_playbooks_live.result has the shape
+    //   "N/N filled → Name A, Name B, Name C"
+    // or historical "Name A | Name B | Name C". Handle both.
+    const counts = new Map<string, number>();
+    for (const row of data.rows ?? []) {
+      const raw = String(row.result ?? "");
+      const afterArrow = raw.includes("→") ? raw.split("→")[1] : raw;
+      const names = afterArrow
+        .split(/[,|]/)
+        .map((n: string) => n.trim())
+        .filter(n => n && !/^\d+\/\d+/.test(n) && !n.toLowerCase().startsWith("filled"));
+      for (const n of names) counts.set(n, (counts.get(n) ?? 0) + 1);
+    }
+    return [...counts.entries()]
+      .sort((a, b) => b[1] - a[1])
+      .slice(0, 5)
+      .map(([name, endorsements]) => ({ name, endorsements }));
+  } catch {
+    return [];
+  }
+}

tests/multi-agent/normalize.test.ts

@@ -0,0 +1,77 @@
+import { test, expect } from "bun:test";
+import { normalizeInputSync, normalizeInput } from "./normalize.ts";
+
+test("structured FillEvent → fast path", () => {
+  const n = normalizeInputSync({
+    kind: "baseline_fill", role: "Welder", city: "Nashville",
+    state: "TN", count: 4,
+  });
+  expect(n.extraction_method).toBe("structured");
+  expect(n.role).toBe("Welder");
+  expect(n.city).toBe("Nashville");
+  expect(n.state).toBe("TN");
+  expect(n.count).toBe(4);
+  expect(n.confidence).toBe("high");
+  expect(n.missing_fields.length).toBe(0);
+});
+
+test("misplacement kind → rescue intent", () => {
+  const n = normalizeInputSync({ kind: "misplacement", role: "Welder", city: "Nashville", state: "TN", count: 1 });
+  expect(n.intent).toBe("rescue");
+});
+
+test("structured with full state name normalizes to abbrev", () => {
+  const n = normalizeInputSync({ role: "Welder", city: "Nashville", state: "Tennessee" });
+  expect(n.state).toBe("TN");
+});
+
+test("natural-language regex path extracts count + role + city + state", () => {
+  const n = normalizeInputSync("I need 3 Welders in Nashville, TN for next week");
+  expect(n.role).toBe("Welder");
+  expect(n.city).toBe("Nashville");
+  expect(n.state).toBe("TN");
+  expect(n.count).toBe(3);
+  expect(n.intent).toBe("fill");
+  expect(n.extraction_method).toBe("regex");
+});
+
+test("natural language with plural role + full state name", () => {
+  const n = normalizeInputSync("fill 5 Forklift Operators in Chicago, Illinois");
+  expect(n.role).toBe("Forklift Operator");
+  expect(n.city).toBe("Chicago");
+  expect(n.state).toBe("IL");
+  expect(n.count).toBe(5);
+});
+
+test("rescue intent keyword beats fill", () => {
+  const n = normalizeInputSync("we need to rescue the Nashville welder fill, 2 workers");
+  expect(n.intent).toBe("rescue");
+  expect(n.count).toBe(2);
+  expect(n.role).toBe("Welder");
+});
+
+test("partial input reports missing fields with confidence=low or medium", () => {
+  const n = normalizeInputSync("need welders");
+  expect(n.role).toBe("Welder");
+  expect(n.city).toBeNull();
+  expect(n.state).toBeNull();
+  expect(n.count).toBeNull();
+  expect(n.missing_fields).toContain("city");
+  expect(n.missing_fields).toContain("state");
+  expect(n.confidence).toBe("low");
+});
+
+test("empty object does NOT go through structured path (no role or city)", () => {
+  const n = normalizeInputSync({});
+  expect(n.extraction_method).toBe("regex");
+});
+
+test("async normalizeInput on clean structured input matches sync", async () => {
+  const input = { role: "Welder", city: "Nashville", state: "TN", count: 3 };
+  const sync = normalizeInputSync(input);
+  const async_ = await normalizeInput(input);
+  expect(async_.role).toBe(sync.role);
+  expect(async_.city).toBe(sync.city);
+  expect(async_.state).toBe(sync.state);
+  expect(async_.count).toBe(sync.count);
+});

tests/multi-agent/normalize.ts

@@ -0,0 +1,296 @@
+// Input normalizer — accepts whatever shape a caller provides (clean
+// structured spec, partial JSON, natural language, a mix) and produces
+// a canonical shape the rest of the pipeline understands. This is the
+// missing piece that lets KB + playbook_memory + overseer respond
+// "seamlessly with whatever input they're given" as J framed it.
+//
+// The pipeline already works when input is `{role, city, state, count}`;
+// what breaks today is "I need three welders in Nashville Monday" —
+// the SQL-filter extractors return None, the boost pipeline sees no
+// role/geo signal, and retrieval silently degrades to raw cosine.
+//
+// Strategy: fast path for structured input, LLM fallback only for
+// natural language. We don't want to burn an LLM call on "role=Welder
+// city=Nashville state=TN count=3" — that's wasteful.
+
+import { generateContinuable } from "./agent.ts";
+
+export interface NormalizedInput {
+  role: string | null;
+  city: string | null;
+  state: string | null;         // two-letter
+  count: number | null;
+  client: string | null;
+  deadline: string | null;       // ISO date or null
+  intent: "fill" | "lookup" | "rescue" | "rebuild" | "unknown";
+  raw_text: string;              // what the caller originally sent
+  confidence: "high" | "medium" | "low";
+  extraction_method: "structured" | "regex" | "llm" | "mixed";
+  missing_fields: string[];      // what normalizer couldn't determine
+}
+
+// US state abbreviations for regex city,state extraction.
+const STATE_ABBRS = new Set([
+  "AL","AK","AZ","AR","CA","CO","CT","DE","FL","GA","HI","ID","IL","IN","IA",
+  "KS","KY","LA","ME","MD","MA","MI","MN","MS","MO","MT","NE","NV","NH","NJ",
+  "NM","NY","NC","ND","OH","OK","OR","PA","RI","SC","SD","TN","TX","UT","VT",
+  "VA","WA","WV","WI","WY","DC",
+]);
+
+const STATE_NAMES_TO_ABBR: Record<string, string> = {
+  alabama: "AL", alaska: "AK", arizona: "AZ", arkansas: "AR", california: "CA",
+  colorado: "CO", connecticut: "CT", delaware: "DE", florida: "FL", georgia: "GA",
+  hawaii: "HI", idaho: "ID", illinois: "IL", indiana: "IN", iowa: "IA",
+  kansas: "KS", kentucky: "KY", louisiana: "LA", maine: "ME", maryland: "MD",
+  massachusetts: "MA", michigan: "MI", minnesota: "MN", mississippi: "MS", missouri: "MO",
+  montana: "MT", nebraska: "NE", nevada: "NV", "new hampshire": "NH", "new jersey": "NJ",
+  "new mexico": "NM", "new york": "NY", "north carolina": "NC", "north dakota": "ND",
+  ohio: "OH", oklahoma: "OK", oregon: "OR", pennsylvania: "PA", "rhode island": "RI",
+  "south carolina": "SC", "south dakota": "SD", tennessee: "TN", texas: "TX", utah: "UT",
+  vermont: "VT", virginia: "VA", washington: "WA", "west virginia": "WV", wisconsin: "WI",
+  wyoming: "WY",
+};
+
+// Canonical staffing roles — the normalizer recognizes these regardless
+// of casing / plurality. Extend as domains grow.
+const KNOWN_ROLES = [
+  "Warehouse Associate", "Machine Operator", "Forklift Operator", "Loader",
+  "Material Handler", "Assembler", "Quality Tech", "Picker", "Packer",
+  "Shipping Clerk", "Receiving Clerk", "Welder", "CNC Operator",
+  "Maintenance Tech", "Electrician", "Tool & Die Maker", "Safety Coordinator",
+  "Logistics Coordinator", "Packaging Operator", "Sanitation Worker",
+  "Line Lead",
+];
+
+// Intent keyword hints. Check in order — "rescue" beats generic "fill"
+// for "rescue the Nashville welder fill" kind of input.
+const INTENT_HINTS: Array<{ re: RegExp; intent: NormalizedInput["intent"] }> = [
+  { re: /\brescue|pivot|retry|remediat/i,       intent: "rescue" },
+  { re: /\brebuild|reindex|re-?embed/i,         intent: "rebuild" },
+  { re: /\blookup|find|show me|who is|what is/i, intent: "lookup" },
+  { re: /\bfill|hire|staff|need|schedule|book/i, intent: "fill" },
+];
+
+// Fast structured path — the input is already a FillEvent-shaped JSON
+// or a close cousin. Returns null to fall through to regex/LLM.
+function fromStructured(raw: any): NormalizedInput | null {
+  if (!raw || typeof raw !== "object") return null;
+  const role = typeof raw.role === "string" ? raw.role : null;
+  const city = typeof raw.city === "string" ? raw.city : null;
+  const stateRaw = typeof raw.state === "string" ? raw.state : null;
+  const count = typeof raw.count === "number" ? raw.count : null;
+  // Only treat as structured if AT LEAST role OR city is present; a
+  // bare {kind: "baseline_fill"} shouldn't go through this path.
+  if (!role && !city) return null;
+  return {
+    role,
+    city,
+    state: normalizeState(stateRaw),
+    count,
+    client: typeof raw.client === "string" ? raw.client : null,
+    deadline: typeof raw.deadline === "string" ? raw.deadline : null,
+    intent: (raw.kind === "misplacement" || /rescue/i.test(raw.kind ?? "")) ? "rescue" : "fill",
+    raw_text: JSON.stringify(raw),
+    confidence: "high",
+    extraction_method: "structured",
+    missing_fields: missingFields(role, city, stateRaw, count),
+  };
+}
+
+function normalizeState(s: string | null | undefined): string | null {
+  if (!s) return null;
+  const trimmed = s.trim();
+  if (STATE_ABBRS.has(trimmed.toUpperCase())) return trimmed.toUpperCase();
+  const full = trimmed.toLowerCase();
+  if (STATE_NAMES_TO_ABBR[full]) return STATE_NAMES_TO_ABBR[full];
+  return null;
+}
+
+function missingFields(role: any, city: any, state: any, count: any): string[] {
+  const out: string[] = [];
+  if (!role) out.push("role");
+  if (!city) out.push("city");
+  if (!state) out.push("state");
+  if (count === null || count === undefined) out.push("count");
+  return out;
+}
+
+// Regex path — handles structured-ish text like "3 welders in Nashville TN".
+// Returns partial extraction; LLM path fills the rest if this leaves
+// too many missing fields.
+function fromRegex(text: string): NormalizedInput {
+  const lower = text.toLowerCase();
+
+  // Count: "3 welders" or "need 5" or "fill 8 safety coordinators"
+  let count: number | null = null;
+  const countMatch = text.match(/\b(?:need|fill|book|hire|schedule|want)\s+(\d+)\b/i)
+                  ?? text.match(/\b(\d+)\s+(?:x\s+)?[A-Za-z]/);
+  if (countMatch) count = parseInt(countMatch[1], 10);
+
+  // Role: longest prefix match from KNOWN_ROLES (case-insensitive).
+  // Plural forms ("welders") match "Welder" via endsWith("s") strip.
+  let role: string | null = null;
+  for (const known of KNOWN_ROLES.sort((a, b) => b.length - a.length)) {
+    const needle = known.toLowerCase();
+    if (lower.includes(needle)) { role = known; break; }
+    if (needle.endsWith("r") && lower.includes(needle + "s")) { role = known; break; }
+  }
+
+  // City, state: "Nashville TN" or "Nashville, Tennessee" or "Nashville, TN".
+  // City capture is 1-3 capitalized words — anything longer is usually
+  // the surrounding phrase ("Forklift Operators in Chicago"). Prefer
+  // "in {city}" anchor when present.
+  let city: string | null = null;
+  let state: string | null = null;
+  const cityPat = `([A-Z][a-zA-Z.'-]+(?:\\s+[A-Z][a-zA-Z.'-]+){0,2})`;
+  // First preference: "in {City}, XX" or "in {City} XX"
+  const inAbbrev = text.match(new RegExp(`\\bin\\s+${cityPat},?\\s+([A-Z]{2})\\b`));
+  if (inAbbrev && STATE_ABBRS.has(inAbbrev[2])) {
+    city = inAbbrev[1].trim();
+    state = inAbbrev[2];
+  } else {
+    // Bare "{City}, XX" (no "in" anchor)
+    const bareAbbrev = text.match(new RegExp(`${cityPat},\\s+([A-Z]{2})\\b`));
+    if (bareAbbrev && STATE_ABBRS.has(bareAbbrev[2])) {
+      city = bareAbbrev[1].trim();
+      state = bareAbbrev[2];
+    } else {
+      // "{City}, FullName" variant. Can't use case-insensitive flag
+      // because it would let cityPat's [A-Z] match lowercase letters.
+      // Instead try both exact-case and Title-case variants.
+      for (const [full, abbr] of Object.entries(STATE_NAMES_TO_ABBR)) {
+        const fullTitle = full.split(" ").map(w => w[0].toUpperCase() + w.slice(1)).join(" ");
+        const variants = [full, fullTitle];
+        for (const variant of variants) {
+          const re = new RegExp(`\\bin\\s+${cityPat},?\\s+${variant}\\b`);
+          const m = text.match(re);
+          if (m) { city = m[1].trim(); state = abbr; break; }
+        }
+        if (city) break;
+        // Bare "{City}, FullName" (no "in")
+        for (const variant of variants) {
+          const re = new RegExp(`${cityPat},\\s+${variant}\\b`);
+          const m = text.match(re);
+          if (m) { city = m[1].trim(); state = abbr; break; }
+        }
+        if (city) break;
+      }
+    }
+  }
+
+  // Intent
+  let intent: NormalizedInput["intent"] = "unknown";
+  for (const h of INTENT_HINTS) {
+    if (h.re.test(text)) { intent = h.intent; break; }
+  }
+
+  const missing = missingFields(role, city, state, count);
+  return {
+    role, city, state, count,
+    client: null,
+    deadline: null,
+    intent,
+    raw_text: text,
+    confidence: missing.length === 0 ? "high" : missing.length <= 2 ? "medium" : "low",
+    extraction_method: "regex",
+    missing_fields: missing,
+  };
+}
+
+// LLM path — when regex leaves too many fields missing, ask a small
+// local model to extract what's present. Uses `think:false` and a
+// strict JSON prompt so the call stays cheap.
+async function fromLLM(text: string, fallback: NormalizedInput): Promise<NormalizedInput> {
+  const prompt = `Extract the staffing request shape from this text. Respond with ONLY a JSON object, no prose.
+
+Text: ${text}
+
+Required shape:
+{
+  "role": "string | null (one of: Welder, Forklift Operator, Warehouse Associate, Machine Operator, Loader, Material Handler, Assembler, Quality Tech, Picker, Packer, Shipping Clerk, Receiving Clerk, CNC Operator, Maintenance Tech, Electrician, Tool & Die Maker, Safety Coordinator, Logistics Coordinator, Packaging Operator, Sanitation Worker, Line Lead, or null if unclear)",
+  "city": "string | null (US city name, properly capitalized)",
+  "state": "string | null (two-letter abbrev like TN, IL)",
+  "count": "number | null (integer number of workers needed)",
+  "client": "string | null (company/client name if mentioned)",
+  "deadline": "string | null (ISO date YYYY-MM-DD if a specific date is mentioned, else null)",
+  "intent": "string (one of: fill, lookup, rescue, rebuild, unknown)"
+}
+
+Return the JSON object now:`;
+
+  try {
+    const raw = await generateContinuable("qwen3:latest", prompt, {
+      max_tokens: 400,
+      shape: "json",
+      think: false,
+      max_continuations: 2,
+    });
+    const match = raw.match(/\{[\s\S]*\}/);
+    if (!match) return fallback;
+    const parsed = JSON.parse(match[0]);
+    const role = typeof parsed.role === "string" && parsed.role !== "null" ? parsed.role : null;
+    const city = typeof parsed.city === "string" && parsed.city !== "null" ? parsed.city : null;
+    const state = normalizeState(parsed.state);
+    const count = typeof parsed.count === "number" ? parsed.count
+                : typeof parsed.count === "string" ? parseInt(parsed.count, 10) || null
+                : null;
+    const client = typeof parsed.client === "string" && parsed.client !== "null" ? parsed.client : null;
+    const deadline = typeof parsed.deadline === "string" && parsed.deadline !== "null" ? parsed.deadline : null;
+    const intent = ["fill", "lookup", "rescue", "rebuild", "unknown"].includes(parsed.intent)
+      ? parsed.intent as NormalizedInput["intent"]
+      : "unknown";
+    const missing = missingFields(role, city, state, count);
+    return {
+      role: role ?? fallback.role,
+      city: city ?? fallback.city,
+      state: state ?? fallback.state,
+      count: count ?? fallback.count,
+      client: client ?? fallback.client,
+      deadline: deadline ?? fallback.deadline,
+      intent: intent !== "unknown" ? intent : fallback.intent,
+      raw_text: text,
+      confidence: missing.length === 0 ? "high" : missing.length <= 2 ? "medium" : "low",
+      extraction_method: "llm",
+      missing_fields: missing,
+    };
+  } catch {
+    return fallback;
+  }
+}
+
+// Top-level normalizer. Auto-detects input shape:
+//   - Object with role/city → fast structured path
+//   - String with enough regex signal → regex path
+//   - Low-signal string → LLM path with regex seed
+export async function normalizeInput(raw: unknown): Promise<NormalizedInput> {
+  // Structured path
+  if (raw && typeof raw === "object") {
+    const s = fromStructured(raw);
+    if (s) return s;
+  }
+  // String path
+  const text = typeof raw === "string" ? raw
+             : raw && typeof raw === "object" ? JSON.stringify(raw)
+             : String(raw);
+  const reg = fromRegex(text);
+  // If regex got at least role + city, trust it.
+  if (reg.role && reg.city) {
+    return reg;
+  }
+  // Otherwise try LLM fallback, using regex result as seed.
+  return await fromLLM(text, reg);
+}
+
+// Synchronous-only variant — returns whatever regex/structured can get
+// without an LLM call. Useful when caller can't await (e.g. a prompt
+// template).
+export function normalizeInputSync(raw: unknown): NormalizedInput {
+  if (raw && typeof raw === "object") {
+    const s = fromStructured(raw);
+    if (s) return s;
+  }
+  const text = typeof raw === "string" ? raw
+             : raw && typeof raw === "object" ? JSON.stringify(raw)
+             : String(raw);
+  return fromRegex(text);
+}