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Sample analysis · 33 fields

One student, end-to-end.

The numbers are fake. The shapes are real. This is exactly what your Slate Query Service sends Celia, what Celia reasons on, and what gets written back into the student’s record in 33 filterable ss_celia_* fields.

Tenant org_ncsu · Flow Summer Melt 2026 — CS Cohort · Model celia-v1.4

1. What Celia saw (from your Slate Query Service)

Anonymized. Behavioral. No names, no emails, no phones, no addresses, no financial account numbers. That is not a policy — it is a structural property of the query itself.

{
  "slate_person_ref":  "P-142098",
  "slate_person_guid": "8f42a91c-0000-4000-8000-000000000000",
  "flow_name":         "Summer Melt 2026 — CS Cohort",

  "stage":            "app_submitted",
  "days_in_stage":    23,
  "docs_missing":     ["fafsa_submitted", "counselor_recommendation_letter"],
  "last_touch":       { "channel": "email", "days_ago": 7, "opened": false },
  "engagement_7d":    { "logins": 3, "portal_clicks": 15,
                        "deepest_page": "/financial-aid/packages" },
  "cohort_base":      { "program_yield_rate": 0.68, "stage_median_days": 9 }
}

PII fields never sent

name · email · phone · address · DOB · SSN · essay text · recommendation letter text

Architectural, not aspirational

Your CIO can verify this at the architecture level — it’s the shape of the query, not a promise we make.

2. What Celia wrote back into Slate

33 structured fields, every one of them filterable, reportable, and list-buildable in Slate. Organized in four Layers — identifiers, the triage list-view, the drill-down, and metadata + counselor feedback. Your team works in Slate; Celia just makes Slate smarter.

Layer 0

Identifiers — 3 fields (join key + Flow context)

slate_person_guid

8f42a91c-0000-4000-8000-000000000000

slate_person_ref

P-142098

ss_celia_flow_name

Summer Melt 2026 — CS Cohort

Layer 1

List view — 5 fields (morning triage)

ss_celia_risk

High

ss_celia_engagement

78

ss_celia_readiness

42

ss_celia_yield

0.61

ss_celia_next_action_1

Send FAFSA reminder today

Layer 2

Student record drill-down — 15 fields

Engagement

78 / 100

↑ above baseline, cadence fading

_BASELINE
66
_DELTA
+12
_CONF
0.84

Readiness

42 / 100

↓ stalled 23 days, 2 blocking items

_BASELINE
65
_DELTA
−23
_CONF
0.91

Yield

0.61 prob

high-likely; readiness drag −0.14

_BASELINE
0.68
_DELTA
−0.07
_CONF
0.78

ss_celia_risk_confidence + _context

0.82 confidence

Historical melt rate for this stage + timeline pattern: 38% if not touched within the next 7 days.

ss_celia_next_action_2

Personal counselor call within 72h — walk through financial-aid timeline

Layer 3

Metadata + feedback — 10 fields

COMPUTED_AT

2026-04-23 02:14 UTC

DATA_AS_OF

2026-04-22 23:59 UTC

NEXT_REFRESH

2026-04-24 02:00 UTC

MODEL_VERSION

celia-v1.4

ss_celia_next_action_3 + deadline

Nudge recommender on letter — offer auto-fill submission link

Deadline: 2026-04-26

ss_celia_next_action_why

FAFSA is 14d overdue; cohort stage-median is 9d. Recommendation letter blocks further progress. Yield probability will drop another ˜10% if both gaps remain unclosed in 7d.

ss_celia_signals_explained

Engagement 78 driven by 3 logins in 7 days (baseline 1.2) and deep financial-aid page browsing; offset by a 7-day unopened email. Readiness 42 pulled down by FAFSA and recommendation letter both overdue. Yield 0.61 high-likely but with readiness drag.

ss_celia_acknowledged_by

null (pending counselor action)

ss_celia_ack_outcome

pending

3. What your counselor opens in Slate

No new tool to learn. No dashboard to babysit. The next morning, the counselor opens the student record in Slate and sees this at the top:

P-142098 · Computer Science · Fall 2026

Jordan R. · s_8f42a91c

Flow: Summer Melt 2026 — CS Cohort

High risk

Engagement

78

↑ +12 vs cohort 66

Readiness

42

↓ −23 vs cohort 65

Yield probability

0.61

↓ −0.07 vs 0.68

Do these today (deadline 2026-04-26)

Send FAFSA reminder · Call within 72h · Nudge recommender

Less than 3 seconds from opening Slate to knowing what to do.

4. The economics, per student

Every Celia run is logged with per-tenant cost attribution. This is what one student cost to analyze on the batch this sample came from.

Input tokens

1,420

Output tokens

380

Batch size

20

Cost / student

$0.00021

Scoring an entire 10,000-student cohort every night runs around $2.10/day.

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