diff --git a/apps/web/src/api/lib/uncertainty/calibration.ts b/apps/web/src/api/lib/uncertainty/calibration.ts
new file mode 100644
index 0000000..015bc4b
--- /dev/null
+++ b/apps/web/src/api/lib/uncertainty/calibration.ts
@@ -0,0 +1,124 @@
+import { eq, sql } from "drizzle-orm";
+import { uuidv7 } from "uuidv7";
+import type { Database } from "../../../db/client";
+import {
+  uncertaintyCalibrationSnapshot,
+  uncertaintyPrediction,
+} from "../../../db/schema/uncertainty";
+import { BUCKET_COUNT, bucketBounds, bucketIndex, brierScore } from "./cohort";
+
+export type CohortRow = {
+  cohortKey: string;
+  claimedConfidence: number;
+  outcomeCorrectness: number | null;
+  state: "emitted" | "witnessed" | "orphaned" | "retired";
+};
+
+export type BucketSnapshot = {
+  cohortKey: string;
+  bucketLower: number;
+  bucketUpper: number;
+  claimedConfidence: number;
+  actualCorrectness: number;
+  predictionCount: number;
+  orphanCount: number;
+  brierScore: number;
+};
+
+/**
+ * Splits witnessed predictions in a cohort into 10 buckets of 0.1 width on
+ * claimed_confidence. Per bucket: mean claimed, mean correctness, count, and
+ * Brier score. Orphan count is per-cohort (not per-bucket) and attached to
+ * every emitted snapshot row so the dashboard can show the orphan share.
+ *
+ * Buckets with zero witnessed predictions are dropped -- a bucket only exists
+ * once at least one prediction has fully resolved.
+ */
+export function bucketCohort(rows: readonly CohortRow[]): BucketSnapshot[] {
+  if (rows.length === 0) return [];
+  const cohortKey = rows[0].cohortKey;
+
+  const witnessed = rows.filter(
+    (r): r is CohortRow & { outcomeCorrectness: number } =>
+      r.state === "witnessed" && r.outcomeCorrectness !== null,
+  );
+  const orphanCount = rows.filter((r) => r.state === "orphaned").length;
+
+  const buckets: { claimed: number[]; correctness: number[] }[] = Array.from(
+    { length: BUCKET_COUNT },
+    () => ({ claimed: [], correctness: [] }),
+  );
+
+  for (const row of witnessed) {
+    const idx = bucketIndex(row.claimedConfidence);
+    buckets[idx].claimed.push(row.claimedConfidence);
+    buckets[idx].correctness.push(row.outcomeCorrectness);
+  }
+
+  const out: BucketSnapshot[] = [];
+  for (let i = 0; i < BUCKET_COUNT; i++) {
+    const b = buckets[i];
+    if (b.claimed.length === 0) continue;
+    const { lower, upper } = bucketBounds(i);
+    const meanClaimed = b.claimed.reduce((a, c) => a + c, 0) / b.claimed.length;
+    const meanCorrectness = b.correctness.reduce((a, c) => a + c, 0) / b.correctness.length;
+    out.push({
+      cohortKey,
+      bucketLower: lower,
+      bucketUpper: upper,
+      claimedConfidence: meanClaimed,
+      actualCorrectness: meanCorrectness,
+      predictionCount: b.claimed.length,
+      orphanCount,
+      brierScore: brierScore(b.claimed, b.correctness),
+    });
+  }
+  return out;
+}
+
+/**
+ * Recompute snapshots for every active cohort. "Active" means the cohort has
+ * at least one resolved (witnessed or orphaned) prediction.
+ */
+export async function rollCalibration(
+  db: Database,
+  now: Date = new Date(),
+): Promise<{ cohorts: number; rows: number }> {
+  const cohortKeys = await db
+    .select({ cohortKey: uncertaintyPrediction.cohortKey })
+    .from(uncertaintyPrediction)
+    .where(sql`${uncertaintyPrediction.state} IN ('witnessed', 'orphaned')`)
+    .groupBy(uncertaintyPrediction.cohortKey);
+
+  let totalRows = 0;
+  for (const { cohortKey } of cohortKeys) {
+    const rows = (await db
+      .select({
+        cohortKey: uncertaintyPrediction.cohortKey,
+        claimedConfidence: uncertaintyPrediction.claimedConfidence,
+        outcomeCorrectness: uncertaintyPrediction.outcomeCorrectness,
+        state: uncertaintyPrediction.state,
+      })
+      .from(uncertaintyPrediction)
+      .where(eq(uncertaintyPrediction.cohortKey, cohortKey))) as CohortRow[];
+
+    const snapshots = bucketCohort(rows);
+
+    await db
+      .delete(uncertaintyCalibrationSnapshot)
+      .where(eq(uncertaintyCalibrationSnapshot.cohortKey, cohortKey));
+
+    if (snapshots.length > 0) {
+      await db.insert(uncertaintyCalibrationSnapshot).values(
+        snapshots.map((s) => ({
+          id: uuidv7(),
+          ...s,
+          computedAt: now,
+        })),
+      );
+      totalRows += snapshots.length;
+    }
+  }
+
+  return { cohorts: cohortKeys.length, rows: totalRows };
+}
diff --git a/apps/web/src/api/routes/uncertainty.ts b/apps/web/src/api/routes/uncertainty.ts
index a5ad7d1..bdbd164 100644
--- a/apps/web/src/api/routes/uncertainty.ts
+++ b/apps/web/src/api/routes/uncertainty.ts
@@ -7,6 +7,7 @@ import { createDb } from "../../db/client";
 import { uncertaintyCalibrationSnapshot, uncertaintyPrediction } from "../../db/schema/uncertainty";
 import { outcomeLabelSchema, surfaceSchema } from "../../db/schema/uncertainty.zod";
 import { cohortKey, DAY_MS, DEFAULT_ORPHAN_TTL_DAYS } from "../lib/uncertainty/cohort";
+import { rollCalibration } from "../lib/uncertainty/calibration";
 import { sweepOrphans } from "../lib/uncertainty/orphan-sweep";
 import { requireAuth } from "../middleware/auth";
 import type { HonoEnv } from "../types";
@@ -142,14 +143,23 @@ const uncertaintyRoutes = new Hono<HonoEnv>()
     return c.json({ surfaces: rows });
   });
 
-const internalRoutes = new Hono<HonoEnv>().post("/orphan-sweep", async (c) => {
-  if (!authorizeCron({ req: { header: (n) => c.req.header(n) }, env: c.env })) {
-    return c.json({ error: "Unauthorized" }, 401);
-  }
-  const db = createDb(c.env.DB);
-  const result = await sweepOrphans(db);
-  return c.json(result);
-});
+const internalRoutes = new Hono<HonoEnv>()
+  .post("/orphan-sweep", async (c) => {
+    if (!authorizeCron({ req: { header: (n) => c.req.header(n) }, env: c.env })) {
+      return c.json({ error: "Unauthorized" }, 401);
+    }
+    const db = createDb(c.env.DB);
+    const result = await sweepOrphans(db);
+    return c.json(result);
+  })
+  .post("/calibration-roll", async (c) => {
+    if (!authorizeCron({ req: { header: (n) => c.req.header(n) }, env: c.env })) {
+      return c.json({ error: "Unauthorized" }, 401);
+    }
+    const db = createDb(c.env.DB);
+    const result = await rollCalibration(db);
+    return c.json(result);
+  });
 
 uncertaintyRoutes.route("/internal", internalRoutes);
 
diff --git a/apps/web/tests/api/lib/uncertainty/calibration.test.ts b/apps/web/tests/api/lib/uncertainty/calibration.test.ts
new file mode 100644
index 0000000..610db69
--- /dev/null
+++ b/apps/web/tests/api/lib/uncertainty/calibration.test.ts
@@ -0,0 +1,97 @@
+import { describe, expect, it } from "vitest";
+import { bucketCohort, type CohortRow } from "../../../../src/api/lib/uncertainty/calibration";
+
+const COHORT = "rafters.color|claude-sonnet-4-7|2026-04-01|0.8";
+
+function row(partial: Partial<CohortRow>): CohortRow {
+  return {
+    cohortKey: COHORT,
+    claimedConfidence: 0.85,
+    outcomeCorrectness: 1,
+    state: "witnessed",
+    ...partial,
+  };
+}
+
+describe("bucketCohort", () => {
+  it("returns no buckets for an empty cohort", () => {
+    expect(bucketCohort([])).toEqual([]);
+  });
+
+  it("drops buckets with zero witnessed predictions", () => {
+    // Single witnessed prediction at 0.85 -> bucket 8 only
+    const out = bucketCohort([row({ claimedConfidence: 0.85, outcomeCorrectness: 1 })]);
+    expect(out).toHaveLength(1);
+    expect(out[0].bucketLower).toBeCloseTo(0.8, 5);
+    expect(out[0].bucketUpper).toBeCloseTo(0.9, 5);
+  });
+
+  it("means claimed and correctness within a bucket", () => {
+    const out = bucketCohort([
+      row({ claimedConfidence: 0.82, outcomeCorrectness: 1 }),
+      row({ claimedConfidence: 0.88, outcomeCorrectness: 0.5 }),
+    ]);
+    expect(out).toHaveLength(1);
+    expect(out[0].claimedConfidence).toBeCloseTo(0.85, 5);
+    expect(out[0].actualCorrectness).toBeCloseTo(0.75, 5);
+    expect(out[0].predictionCount).toBe(2);
+  });
+
+  it("computes Brier as mean squared error within the bucket", () => {
+    const out = bucketCohort([
+      row({ claimedConfidence: 0.8, outcomeCorrectness: 1 }), // (0.8-1)^2 = 0.04
+      row({ claimedConfidence: 0.85, outcomeCorrectness: 0 }), // (0.85-0)^2 = 0.7225
+    ]);
+    expect(out[0].brierScore).toBeCloseTo((0.04 + 0.7225) / 2, 5);
+  });
+
+  it("ignores witnessed rows with null correctness", () => {
+    const out = bucketCohort([
+      row({ claimedConfidence: 0.85, outcomeCorrectness: null, state: "witnessed" }),
+      row({ claimedConfidence: 0.85, outcomeCorrectness: 1 }),
+    ]);
+    expect(out).toHaveLength(1);
+    expect(out[0].predictionCount).toBe(1);
+  });
+
+  it("ignores emitted-but-not-witnessed rows but does not double-count", () => {
+    const out = bucketCohort([
+      row({ claimedConfidence: 0.85, outcomeCorrectness: null, state: "emitted" }),
+      row({ claimedConfidence: 0.85, outcomeCorrectness: 1 }),
+    ]);
+    expect(out).toHaveLength(1);
+    expect(out[0].predictionCount).toBe(1);
+  });
+
+  it("attaches per-cohort orphan_count to every emitted bucket row", () => {
+    const out = bucketCohort([
+      row({ claimedConfidence: 0.25, outcomeCorrectness: 1 }),
+      row({ claimedConfidence: 0.85, outcomeCorrectness: 0.5 }),
+      row({ claimedConfidence: 0.85, outcomeCorrectness: null, state: "orphaned" }),
+      row({ claimedConfidence: 0.85, outcomeCorrectness: null, state: "orphaned" }),
+    ]);
+    expect(out).toHaveLength(2);
+    for (const snapshot of out) {
+      expect(snapshot.orphanCount).toBe(2);
+    }
+  });
+
+  it("places confidence=1.0 in the top bucket", () => {
+    const out = bucketCohort([row({ claimedConfidence: 1, outcomeCorrectness: 1 })]);
+    expect(out).toHaveLength(1);
+    expect(out[0].bucketLower).toBeCloseTo(0.9, 5);
+  });
+
+  it("returns buckets sorted by lower bound", () => {
+    const out = bucketCohort([
+      row({ claimedConfidence: 0.95, outcomeCorrectness: 1 }),
+      row({ claimedConfidence: 0.15, outcomeCorrectness: 0 }),
+      row({ claimedConfidence: 0.55, outcomeCorrectness: 0.5 }),
+    ]);
+    expect(out.map((s) => s.bucketLower)).toEqual([
+      expect.closeTo(0.1, 5),
+      expect.closeTo(0.5, 5),
+      expect.closeTo(0.9, 5),
+    ]);
+  });
+});
diff --git a/apps/web/wrangler.jsonc b/apps/web/wrangler.jsonc
index 3432819..ccf12ea 100644
--- a/apps/web/wrangler.jsonc
+++ b/apps/web/wrangler.jsonc
@@ -47,11 +47,12 @@
   "vars": {
     "CRON_SECRET": "",
   },
-  // Hourly orphan sweep. The Astro adapter does not expose a scheduled()
-  // entrypoint, so the trigger calls POST /api/uncertainty/internal/orphan-sweep
-  // via an external scheduler (or a future companion worker) using the
-  // CRON_SECRET bearer token.
+  // Hourly orphan sweep + nightly calibration roll. The Astro adapter does
+  // not expose a scheduled() entrypoint, so triggers call the internal
+  // endpoints (/api/uncertainty/internal/orphan-sweep at :00 every hour,
+  // /api/uncertainty/internal/calibration-roll at 03:15 UTC daily) via an
+  // external scheduler using the CRON_SECRET bearer token.
   "triggers": {
-    "crons": ["0 * * * *"],
+    "crons": ["0 * * * *", "15 3 * * *"],
   },
 }