# ReviewIQ: Review Intelligence Pipeline

**Version**: 3.1.1
**Status**: Architecture Specification (Reviewed)
**Date**: 2026-01-24

---

## Executive Summary

ReviewIQ v3.1 transforms Google Reviews into actionable business intelligence through a scalable, KPI-ready pipeline. This version refactors the data model for multi-location support, replaces array-based aggregation with proper relational design, and introduces a unified analytics spine for fast reporting.

**What's New in v3.1**:
- **Relational issue tracking**: `issue_spans` table replaces `review_ids[]` arrays
- **Multi-location ready**: `place_id` as first-class key throughout
- **Split storage**: `reviews_raw` + `reviews_enriched` for audit and reprocessing
- **Unified analytics spine**: `fact_timeseries` with universal join keys
- **Quality controls**: `trust_score` and `dedup_group_id` for spam filtering
- **Competitor analysis**: Same pipeline, separate tracking table
- **KPI-ready hooks**: Nullable columns for future business metric integration

**Design Principles**:
- **Google Reviews only** (for now) — but schema is source-agnostic
- **Relational over arrays** — scales, queries, joins
- **Facts-first reporting** — pre-aggregated spine for fast dashboards
- **KPI-joinable** — `(business_id, place_id, period_date, bucket_type)` as universal key
- **Tenant-scoped locations** — same place_id can exist for multiple businesses

---

## Part 1: System Architecture

```
┌─────────────────────────────────────────────────────────────────────────────────────┐
│                              REVIEWIQ v3.1 PIPELINE                                  │
├─────────────────────────────────────────────────────────────────────────────────────┤
│                                                                                      │
│  ┌──────────────┐                                                                   │
│  │    Google    │                                                                   │
│  │   Reviews    │                                                                   │
│  │    (API)     │                                                                   │
│  └──────┬───────┘                                                                   │
│         │                                                                           │
│         ▼                                                                           │
│  ┌─────────────────────────────────────────────────────────────────────────────┐   │
│  │                           A) SOURCE & STORAGE                                │   │
│  │                                                                              │   │
│  │   google_connector  ───▶  reviews_raw (immutable JSON + metadata)           │   │
│  │                                                                              │   │
│  └──────────────────────────────────┬──────────────────────────────────────────┘   │
│                                     │                                               │
│                                     ▼                                               │
│  ┌─────────────────────────────────────────────────────────────────────────────┐   │
│  │                           B) ENRICHMENT                                      │   │
│  │                                                                              │   │
│  │   ┌──────────┐   ┌──────────┐   ┌──────────┐   ┌──────────┐                 │   │
│  │   │Normalize │──▶│   LLM    │──▶│  Embed   │──▶│  Trust   │                 │   │
│  │   │  + Map   │   │ Classify │   │ (local)  │   │  Score   │                 │   │
│  │   └──────────┘   └──────────┘   └──────────┘   └──────────┘                 │   │
│  │         │                                            │                       │   │
│  │         └────────────────────┬───────────────────────┘                       │   │
│  │                              ▼                                               │   │
│  │                     reviews_enriched (versioned)                             │   │
│  │                                                                              │   │
│  └──────────────────────────────┬──────────────────────────────────────────────┘   │
│                                 │                                                   │
│                    ┌────────────┴────────────┐                                     │
│                    ▼                         ▼                                      │
│  ┌────────────────────────────┐  ┌────────────────────────────────┐                │
│  │   C) OPERATIONALIZATION    │  │      D) ANALYTICS SPINE        │                │
│  │                            │  │                                │                │
│  │  reviews_enriched          │  │  Daily/Weekly Jobs:            │                │
│  │       │                    │  │                                │                │
│  │       ▼                    │  │  reviews_enriched              │                │
│  │  Match/Create Issue        │  │       │                        │                │
│  │       │                    │  │       ▼                        │                │
│  │       ▼                    │  │  fact_timeseries               │                │
│  │  issue_spans (link)        │  │  (pre-aggregated metrics)      │                │
│  │       │                    │  │                                │                │
│  │       ▼                    │  │  Keys:                         │                │
│  │  issues (update counters)  │  │  • business_id                 │                │
│  │       │                    │  │  • place_id (or 'ALL')         │                │
│  │       ▼                    │  │  • subject_type/id             │                │
│  │  issue_events (log)        │  │  • period_date                 │                │
│  │                            │  │  • bucket_type                 │                │
│  └────────────────────────────┘  └────────────────────────────────┘                │
│                    │                         │                                      │
│                    └────────────┬────────────┘                                     │
│                                 ▼                                                   │
│  ┌─────────────────────────────────────────────────────────────────────────────┐   │
│  │                           E) REPORTING                                       │   │
│  │                                                                              │   │
│  │   fact_timeseries  ──┬──▶  Statistics & Trends                              │   │
│  │                      │                                                       │   │
│  │   issues + spans  ───┼──▶  Issue Rankings & Drill-Down                      │   │
│  │                      │                                                       │   │
│  │   embeddings  ───────┼──▶  Sub-Pattern Clustering                           │   │
│  │                      │                                                       │   │
│  │   competitors  ──────┴──▶  Benchmark Comparisons                            │   │
│  │                      │                                                       │   │
│  │                      ▼                                                       │   │
│  │              LLM Narrative Generation                                        │   │
│  │                                                                              │   │
│  └─────────────────────────────────────────────────────────────────────────────┘   │
│                                                                                      │
└─────────────────────────────────────────────────────────────────────────────────────┘
```

---

## Part 2: Data Model (SQL DDL)

### 2.1 Dimension Tables

```sql
-- Business locations (multi-tenant: same place_id can exist for multiple businesses)
CREATE TABLE locations (
    business_id         TEXT NOT NULL,          -- Internal business identifier
    place_id            TEXT NOT NULL,          -- Google Place ID
    display_name        TEXT NOT NULL,
    address             TEXT,
    city                TEXT,
    state               TEXT,
    country             TEXT,
    timezone            TEXT,
    is_active           BOOLEAN DEFAULT TRUE,
    created_at          TIMESTAMP DEFAULT NOW(),
    updated_at          TIMESTAMP DEFAULT NOW(),

    PRIMARY KEY (business_id, place_id)
);

CREATE INDEX idx_locations_place ON locations(place_id);

-- URT code reference
CREATE TABLE urt_codes (
    code                TEXT PRIMARY KEY,       -- 'J1.01', 'P1.02', etc.
    domain              CHAR(1) NOT NULL,       -- O, P, J, E, A, V, R
    category            TEXT NOT NULL,
    subcategory         TEXT NOT NULL,
    display_name        TEXT NOT NULL,
    description         TEXT,
    keywords            TEXT[]                  -- For search/matching
);

-- Competitor mapping (separate from locations - no fake business_ids)
CREATE TABLE competitors (
    id                  SERIAL PRIMARY KEY,
    business_id         TEXT NOT NULL,          -- Your business
    competitor_place_id TEXT NOT NULL,          -- Competitor's Google Place ID
    competitor_name     TEXT NOT NULL,
    relationship        TEXT DEFAULT 'direct',  -- 'direct', 'indirect', 'aspirational'
    is_active           BOOLEAN DEFAULT TRUE,
    created_at          TIMESTAMP DEFAULT NOW(),

    UNIQUE(business_id, competitor_place_id)
);

CREATE INDEX idx_competitors_business ON competitors(business_id);
```

### 2.2 Reviews Tables (Raw + Enriched)

```sql
-- Immutable raw review storage (audit + reprocessing)
CREATE TABLE reviews_raw (
    id                  SERIAL PRIMARY KEY,
    source              TEXT NOT NULL DEFAULT 'google',
    review_id           TEXT NOT NULL,          -- Google review ID
    place_id            TEXT NOT NULL,          -- Google Place ID

    -- Raw payload
    raw_payload         JSONB NOT NULL,         -- Complete API response
    review_text         TEXT,                   -- Extracted for indexing
    rating              SMALLINT,
    review_time         TIMESTAMP,
    reviewer_name       TEXT,
    reviewer_id         TEXT,

    -- Versioning (Google reviews can be edited)
    review_version      INT DEFAULT 1,

    -- Ingestion metadata
    pulled_at           TIMESTAMP DEFAULT NOW(),

    UNIQUE(source, review_id, review_version)
);

CREATE INDEX idx_reviews_raw_place ON reviews_raw(place_id, review_time DESC);
CREATE INDEX idx_reviews_raw_lookup ON reviews_raw(source, review_id);

-- Enriched review with LLM classification + embeddings (versioned)
-- Supports edited reviews: each version is a separate row
CREATE TABLE reviews_enriched (
    -- Versioned primary key (handles edited reviews)
    source              TEXT NOT NULL DEFAULT 'google',
    review_id           TEXT NOT NULL,          -- Matches reviews_raw.review_id
    review_version      INT NOT NULL DEFAULT 1,
    is_latest           BOOLEAN NOT NULL DEFAULT TRUE,

    -- Link to raw (specific version)
    raw_id              INT NOT NULL REFERENCES reviews_raw(id),

    -- Identity
    business_id         TEXT NOT NULL,
    place_id            TEXT NOT NULL,

    -- Core content
    text                TEXT NOT NULL,
    text_normalized     TEXT,                   -- Cleaned for processing
    rating              SMALLINT,
    review_time         TIMESTAMP NOT NULL,
    language            TEXT,

    -- URT Classification (from LLM)
    urt_primary         TEXT NOT NULL,          -- 'J1.01', 'P1.02', etc.
    urt_secondary       TEXT[] DEFAULT '{}',    -- Max 2, different domains
    valence             TEXT NOT NULL,          -- 'V+', 'V-', 'V0', 'V±'
    intensity           TEXT NOT NULL,          -- 'I1', 'I2', 'I3'
    comparative         TEXT DEFAULT 'CR-N',    -- 'CR-N', 'CR-B', 'CR-W', 'CR-S'

    -- Extracted entities (reserved for v3.2, nullable)
    staff_mentions      TEXT[] DEFAULT '{}',
    quotes              JSONB,                  -- {"code": "phrase", ...}

    -- Embedding
    embedding           VECTOR(384),

    -- Quality control
    trust_score         FLOAT DEFAULT 1.0,      -- 0.0 to 1.0
    dedup_group_id      TEXT,                   -- Groups duplicate/near-duplicate reviews
    is_suspicious       BOOLEAN DEFAULT FALSE,

    -- Processing metadata
    classification_model    TEXT,
    classification_confidence JSONB,            -- Per-field confidence scores
    processed_at        TIMESTAMP DEFAULT NOW(),
    model_version       TEXT,

    -- KPI-ready hooks (nullable, computed later)
    kpi_impact_estimate     FLOAT,
    kpi_last_computed_at    TIMESTAMP,

    PRIMARY KEY (source, review_id, review_version)
);

-- Indexes for common query patterns
CREATE INDEX idx_enriched_latest ON reviews_enriched(source, review_id)
    WHERE is_latest = TRUE;
CREATE INDEX idx_enriched_business_date ON reviews_enriched(business_id, review_time DESC)
    WHERE is_latest = TRUE;
CREATE INDEX idx_enriched_place_date ON reviews_enriched(place_id, review_time DESC)
    WHERE is_latest = TRUE;
CREATE INDEX idx_enriched_urt_primary ON reviews_enriched(business_id, urt_primary)
    WHERE is_latest = TRUE;
CREATE INDEX idx_enriched_valence ON reviews_enriched(business_id, valence, review_time)
    WHERE is_latest = TRUE;
CREATE INDEX idx_enriched_comparative ON reviews_enriched(comparative)
    WHERE comparative != 'CR-N' AND is_latest = TRUE;
CREATE INDEX idx_enriched_trust ON reviews_enriched(trust_score)
    WHERE trust_score < 0.5 AND is_latest = TRUE;
CREATE INDEX idx_enriched_embedding ON reviews_enriched
    USING hnsw (embedding vector_cosine_ops);

-- FK to locations (tenant-scoped)
ALTER TABLE reviews_enriched
    ADD CONSTRAINT fk_enriched_location
    FOREIGN KEY (business_id, place_id) REFERENCES locations(business_id, place_id);
```

### 2.3 Issue Tables (Relational, No Arrays)

**Note**: In v3.1, the issue grouping key is `(business_id, place_id, primary_subcode)` only. The `entity` and `entity_normalized` fields are reserved for v3.2+ when entity extraction is implemented.

```sql
-- Issues (aggregated problems)
CREATE TABLE issues (
    issue_id            TEXT PRIMARY KEY,

    -- Grouping keys (v3.1: code + place only)
    business_id         TEXT NOT NULL,
    place_id            TEXT NOT NULL,
    primary_subcode     TEXT NOT NULL,          -- URT code
    domain              CHAR(1) NOT NULL,

    -- State machine
    state               TEXT NOT NULL DEFAULT 'DETECTED',
    priority_score      FLOAT NOT NULL,
    confidence_score    FLOAT NOT NULL,

    -- Aggregated metrics (updated via triggers/jobs)
    span_count          INT NOT NULL DEFAULT 1,
    max_intensity       TEXT NOT NULL,
    avg_trust_score     FLOAT DEFAULT 1.0,

    -- CR counters (rolling 30-day window)
    cr_better_count     INT DEFAULT 0,
    cr_worse_count      INT DEFAULT 0,
    cr_same_count       INT DEFAULT 0,

    -- Star drag proxy (avg rating when this issue present vs absent)
    star_drag_estimate  FLOAT,

    -- Ownership
    owner_team          TEXT,
    owner_individual    TEXT,

    -- Timestamps
    created_at          TIMESTAMP DEFAULT NOW(),
    updated_at          TIMESTAMP DEFAULT NOW(),
    acknowledged_at     TIMESTAMP,
    resolved_at         TIMESTAMP,
    verified_at         TIMESTAMP,

    -- Resolution
    reopen_count        INT DEFAULT 0,
    resolution_code     TEXT,
    resolution_notes    TEXT,
    decline_reason      TEXT,

    -- Context (RESERVED for v3.2 - entity extraction)
    entity              TEXT,                   -- Product, staff member, feature
    entity_normalized   TEXT,

    -- KPI-ready hooks (nullable)
    kpi_impact_estimate     FLOAT,
    kpi_impact_confidence   FLOAT,
    kpi_last_computed_at    TIMESTAMP
);

CREATE INDEX idx_issues_business ON issues(business_id, state, priority_score DESC);
CREATE INDEX idx_issues_place ON issues(place_id, state);
CREATE INDEX idx_issues_code ON issues(business_id, primary_subcode);
CREATE INDEX idx_issues_open ON issues(business_id)
    WHERE state NOT IN ('VERIFIED', 'DECLINED');

-- FK to locations (tenant-scoped)
ALTER TABLE issues
    ADD CONSTRAINT fk_issues_location
    FOREIGN KEY (business_id, place_id) REFERENCES locations(business_id, place_id);

-- Issue spans (link table: issue ↔ review)
CREATE TABLE issue_spans (
    id                  SERIAL PRIMARY KEY,
    issue_id            TEXT NOT NULL REFERENCES issues(issue_id) ON DELETE CASCADE,
    review_id           TEXT NOT NULL,          -- Matches reviews_enriched (source, review_id, version)

    -- Span metadata
    is_primary_match    BOOLEAN DEFAULT TRUE,   -- Primary vs secondary code match
    weight              FLOAT DEFAULT 1.0,      -- For weighted aggregation
    intensity           TEXT NOT NULL,          -- Copied from review for fast queries

    -- Denormalized for timeline queries (avoids join)
    review_time         TIMESTAMP NOT NULL,

    created_at          TIMESTAMP DEFAULT NOW(),

    UNIQUE(issue_id, review_id)
);

CREATE INDEX idx_spans_issue ON issue_spans(issue_id);
CREATE INDEX idx_spans_review ON issue_spans(review_id);
CREATE INDEX idx_spans_issue_time ON issue_spans(issue_id, review_time DESC);

-- Issue events (audit log)
CREATE TABLE issue_events (
    event_id            SERIAL PRIMARY KEY,
    issue_id            TEXT NOT NULL REFERENCES issues(issue_id),

    event_type          TEXT NOT NULL,          -- 'state_change', 'span_added', 'priority_update'
    from_state          TEXT,
    to_state            TEXT,

    actor               TEXT,                   -- User or 'system'
    notes               TEXT,
    review_id           TEXT,                   -- Triggering review if applicable

    metadata            JSONB,                  -- Additional context
    created_at          TIMESTAMP DEFAULT NOW()
);

CREATE INDEX idx_events_issue ON issue_events(issue_id, created_at DESC);
```

### 2.4 Unified Analytics Spine

**Design Decision**: `place_id = 'ALL'` is the sentinel for "all locations" rollups. This avoids NULL handling complexity while keeping the schema simple.

```sql
-- Fact table: pre-aggregated time-series metrics
CREATE TABLE fact_timeseries (
    id                  SERIAL PRIMARY KEY,

    -- Universal join keys (KPI-ready)
    business_id         TEXT NOT NULL,
    place_id            TEXT NOT NULL,          -- 'ALL' = all locations rollup
    period_date         DATE NOT NULL,
    bucket_type         TEXT NOT NULL,          -- 'day', 'week', 'month'

    -- Subject (what we're measuring)
    subject_type        TEXT NOT NULL,          -- 'urt_code', 'domain', 'overall'
    subject_id          TEXT NOT NULL,          -- Code, domain letter, or 'all'

    -- Volume metrics
    review_count        INT NOT NULL DEFAULT 0,
    negative_count      INT NOT NULL DEFAULT 0,
    positive_count      INT NOT NULL DEFAULT 0,
    neutral_count       INT NOT NULL DEFAULT 0,
    mixed_count         INT NOT NULL DEFAULT 0,

    -- Strength metrics (intensity-weighted)
    strength_score      FLOAT NOT NULL DEFAULT 0,
    negative_strength   FLOAT NOT NULL DEFAULT 0,
    positive_strength   FLOAT NOT NULL DEFAULT 0,

    -- Rating metrics
    avg_rating          FLOAT,
    rating_count        INT DEFAULT 0,

    -- Intensity distribution
    i1_count            INT DEFAULT 0,
    i2_count            INT DEFAULT 0,
    i3_count            INT DEFAULT 0,

    -- CR signals
    cr_better           INT DEFAULT 0,
    cr_worse            INT DEFAULT 0,
    cr_same             INT DEFAULT 0,

    -- Trust-weighted variants
    trust_weighted_strength     FLOAT,
    trust_weighted_negative     FLOAT,

    -- Metadata
    computed_at         TIMESTAMP DEFAULT NOW(),

    UNIQUE(business_id, place_id, period_date, bucket_type, subject_type, subject_id)
);

-- Validate 'ALL' sentinel
ALTER TABLE fact_timeseries
    ADD CONSTRAINT chk_place_id_format
    CHECK (place_id = 'ALL' OR place_id ~ '^[a-zA-Z0-9_-]+$');

-- Optimized indexes for reporting queries
CREATE INDEX idx_facts_lookup ON fact_timeseries(
    business_id, place_id, subject_type, subject_id, period_date DESC
);
CREATE INDEX idx_facts_period ON fact_timeseries(
    business_id, period_date, bucket_type
);
CREATE INDEX idx_facts_code ON fact_timeseries(subject_type, subject_id)
    WHERE subject_type = 'urt_code';
CREATE INDEX idx_facts_all_locations ON fact_timeseries(business_id, period_date)
    WHERE place_id = 'ALL';
```

**v3.1 Fact Population Scope**:
| subject_type | Populated | Notes |
|--------------|-----------|-------|
| `overall` | ✅ Mandatory | Business-wide + per-location |
| `urt_code` | ✅ Mandatory | Per URT code |
| `domain` | ⚡ Derived | Rollup from urt_code at query time |
| `issue` | 🔜 Optional | Recommended for issue timelines (v3.2) |

### 2.5 Sub-Patterns (Persistent Clustering Results)

```sql
-- Stored sub-pattern clustering results
CREATE TABLE subpatterns (
    id                  SERIAL PRIMARY KEY,

    -- Parent
    subject_type        TEXT NOT NULL,          -- 'urt_code', 'issue'
    subject_id          TEXT NOT NULL,
    business_id         TEXT NOT NULL,
    place_id            TEXT,                   -- NULL = all locations

    -- Period
    period_start        DATE NOT NULL,
    period_end          DATE NOT NULL,

    -- Cluster info
    cluster_id          INT NOT NULL,
    label               TEXT NOT NULL,

    -- Metrics
    review_count        INT NOT NULL,
    percentage          FLOAT NOT NULL,
    avg_intensity       FLOAT,

    -- Representative content
    representative_review_id    TEXT,
    representative_quote        TEXT,
    sharpest_review_id          TEXT,
    sharpest_quote              TEXT,

    -- Embedding (for trend matching)
    centroid            VECTOR(384),

    -- Metadata
    computed_at         TIMESTAMP DEFAULT NOW(),

    UNIQUE(subject_type, subject_id, business_id, place_id, period_start, period_end, cluster_id)
);

CREATE INDEX idx_subpatterns_lookup ON subpatterns(
    subject_type, subject_id, business_id, period_end DESC
);
```

---

## Part 3: Ingest Layer

### 3.1 Google Connector

```python
async def pull_reviews(place_id: str, since: datetime = None) -> list[dict]:
    """Fetch new/updated reviews from Google Places API."""

    reviews = await google_places_client.get_reviews(place_id, since=since)

    for review in reviews:
        await store_raw_review(place_id, review)

    return reviews


async def store_raw_review(place_id: str, review: dict) -> int:
    """Store immutable raw review payload."""

    existing = await db.query_one("""
        SELECT id, review_version FROM reviews_raw
        WHERE source = 'google' AND review_id = %s
        ORDER BY review_version DESC LIMIT 1
    """, [review['review_id']])

    version = 1
    if existing:
        if content_changed(existing, review):
            version = existing['review_version'] + 1
        else:
            return existing['id']

    return await db.insert("""
        INSERT INTO reviews_raw (
            source, review_id, place_id, raw_payload,
            review_text, rating, review_time, reviewer_name, reviewer_id,
            review_version, pulled_at
        ) VALUES (
            'google', %s, %s, %s,
            %s, %s, %s, %s, %s,
            %s, NOW()
        )
        RETURNING id
    """, [
        review['review_id'], place_id, json.dumps(review),
        review.get('text'), review.get('rating'),
        review.get('time'), review.get('author_name'), review.get('author_id'),
        version
    ])
```

### 3.2 Enrichment Pipeline

```python
async def enrich_review(raw_id: int) -> dict:
    """Full enrichment: normalize → classify → embed → trust score."""

    raw = await db.query_one(
        "SELECT * FROM reviews_raw WHERE id = %s", [raw_id]
    )

    # 1. Normalize
    text = normalize_text(raw['review_text'])

    # 2. Map to business
    location = await db.query_one(
        "SELECT business_id FROM locations WHERE place_id = %s",
        [raw['place_id']]
    )

    # 3. Parallel: LLM classify + embed
    classify_task = asyncio.create_task(classify_review_llm(text))
    embed_task = asyncio.create_task(embed_review(text))

    classification = await classify_task
    embedding = await embed_task

    # 4. Trust score
    trust_score = compute_trust_score(raw, text, classification)

    # 5. Dedup check
    dedup_group_id = await find_dedup_group(embedding, raw['place_id'])

    # 6. Mark previous versions as not-latest
    await db.execute("""
        UPDATE reviews_enriched
        SET is_latest = FALSE
        WHERE source = 'google' AND review_id = %s AND is_latest = TRUE
    """, [raw['review_id']])

    # 7. Store enriched (versioned)
    enriched = {
        'source': 'google',
        'review_id': raw['review_id'],
        'review_version': raw['review_version'],
        'is_latest': True,
        'raw_id': raw_id,
        'business_id': location['business_id'],
        'place_id': raw['place_id'],
        'text': raw['review_text'],
        'text_normalized': text,
        'rating': raw['rating'],
        'review_time': raw['review_time'],
        'language': detect_language(text),
        'embedding': embedding,
        'trust_score': trust_score,
        'dedup_group_id': dedup_group_id,
        **classification,
    }

    await upsert_enriched_review(enriched)

    return enriched
```

### 3.3 LLM Classification

```python
CLASSIFICATION_PROMPT = """You are a customer feedback classifier using the Universal Review Taxonomy (URT).

Analyze the review and return JSON:

{
  "urt_primary": "X1.23",
  "urt_secondary": ["Y2.34"],
  "valence": "V-",
  "intensity": "I2",
  "comparative": "CR-N",
  "staff_mentions": ["Mike"],
  "quotes": {
    "X1.23": "exact phrase",
    "Y2.34": "another phrase"
  },
  "confidence": {
    "urt_primary": 0.92,
    "valence": 0.95,
    "intensity": 0.88
  }
}

URT DOMAINS:
- O (Offering): Product/service quality, function, completeness
- P (People): Staff attitude, competence, responsiveness
- J (Journey): Timing, ease, reliability, resolution
- E (Environment): Physical space, digital interface, ambiance
- A (Access): Availability, accessibility, convenience
- V (Value): Price, transparency, worth
- R (Relationship): Trust, dependability, loyalty

RULES:
1. Primary = most impactful topic
2. Secondary must be DIFFERENT domains
3. V± only for genuinely mixed sentiment
4. CR-B/W/S only for explicit self-comparison
5. quotes = EXACT phrases from review

Return valid JSON only."""


async def classify_review_llm(text: str) -> dict:
    """LLM-powered URT classification."""

    response = await llm.chat(
        model="gpt-4o-mini",
        messages=[
            {"role": "system", "content": CLASSIFICATION_PROMPT},
            {"role": "user", "content": text}
        ],
        response_format={"type": "json_object"},
        temperature=0.1
    )

    result = json.loads(response.content)
    result['classification_model'] = 'gpt-4o-mini'

    return result
```

### 3.4 Trust Score Computation

```python
def compute_trust_score(raw: dict, text: str, classification: dict) -> float:
    """
    Compute trust score (0.0 to 1.0) based on review quality signals.
    Low trust = likely spam, fake, or low-quality.
    """
    score = 1.0

    # Length penalty
    word_count = len(text.split())
    if word_count < 5:
        score *= 0.5
    elif word_count > 500:
        score *= 0.8

    # Rating/sentiment mismatch
    rating = raw.get('rating')
    valence = classification.get('valence')
    if rating and valence:
        if rating >= 4 and valence == 'V-':
            score *= 0.7
        elif rating <= 2 and valence == 'V+':
            score *= 0.7

    # Generic text patterns
    if is_generic_review(text):
        score *= 0.6

    # LLM confidence
    confidence = classification.get('confidence', {})
    avg_confidence = np.mean(list(confidence.values())) if confidence else 1.0
    if avg_confidence < 0.7:
        score *= 0.9

    return max(0.1, min(1.0, score))
```

---

## Part 4: Issue Lifecycle Management

### 4.1 Issue Aggregation (Relational)

**v3.1 Issue Key**: `(business_id, place_id, primary_subcode)` — entity matching is deferred to v3.2.

```python
async def aggregate_to_issue(review: dict) -> Optional[str]:
    """Match review to existing issue or create new one."""

    if review['valence'] not in ('V-', 'V±'):
        return None

    # Find matching open issues using relational query
    # v3.1: match on (business_id, place_id, primary_subcode) only
    matching = await db.query("""
        SELECT i.issue_id, i.primary_subcode, i.span_count
        FROM issues i
        WHERE i.business_id = %s
          AND i.place_id = %s
          AND i.primary_subcode = %s
          AND i.state NOT IN ('VERIFIED', 'DECLINED')
          AND i.created_at > NOW() - INTERVAL '30 days'
        ORDER BY i.priority_score DESC
        LIMIT 1
    """, [review['business_id'], review['place_id'], review['urt_primary']])

    if matching:
        issue = matching[0]
        await add_span_to_issue(issue['issue_id'], review)
        return issue['issue_id']

    if should_create_issue(review):
        return await create_issue(review)

    return None


async def add_span_to_issue(issue_id: str, review: dict):
    """Add review span to issue and update counters."""

    # Insert span (with denormalized review_time for timeline queries)
    await db.execute("""
        INSERT INTO issue_spans (issue_id, review_id, is_primary_match, intensity, review_time)
        VALUES (%s, %s, %s, %s, %s)
        ON CONFLICT (issue_id, review_id) DO NOTHING
    """, [issue_id, review['review_id'], True, review['intensity'], review['review_time']])

    # Update issue counters
    await db.execute("""
        UPDATE issues SET
            span_count = (SELECT COUNT(*) FROM issue_spans WHERE issue_id = %s),
            max_intensity = (
                SELECT CASE MAX(CASE intensity
                    WHEN 'I3' THEN 3 WHEN 'I2' THEN 2 ELSE 1 END)
                    WHEN 3 THEN 'I3' WHEN 2 THEN 'I2' ELSE 'I1' END
                FROM issue_spans WHERE issue_id = %s
            ),
            updated_at = NOW()
        WHERE issue_id = %s
    """, [issue_id, issue_id, issue_id])

    await recalculate_priority(issue_id)
    await log_issue_event(issue_id, 'span_added', review_id=review['review_id'])
```

### 4.2 Priority Scoring (Trust-Weighted)

```python
INTENSITY_WEIGHTS = {'I1': 1.0, 'I2': 2.0, 'I3': 4.0}

async def recalculate_priority(issue_id: str):
    """
    Priority = intensity × volume × decay × recurrence × trend × trust
    """

    issue = await db.query_one("""
        SELECT
            i.*,
            (SELECT AVG(re.trust_score)
             FROM issue_spans s
             JOIN reviews_enriched re ON s.review_id = re.review_id
             WHERE s.issue_id = i.issue_id AND re.is_latest = TRUE) as avg_trust
        FROM issues i
        WHERE i.issue_id = %s
    """, [issue_id])

    intensity_num = {'I1': 1, 'I2': 2, 'I3': 3}.get(issue['max_intensity'], 1)
    i_weight = INTENSITY_WEIGHTS.get(f"I{intensity_num}", 1.0)

    volume_factor = 1 + math.log(max(1, issue['span_count']))

    days_old = (datetime.now() - issue['created_at']).days
    decay = math.exp(-0.023 * days_old)

    recurrence_boost = 1.0 + 0.5 * math.log2(issue['reopen_count'] + 1)

    if issue['cr_worse_count'] >= 2:
        trend_modifier = 1.3
    elif issue['cr_better_count'] >= 2:
        trend_modifier = 0.7
    else:
        trend_modifier = 1.0

    trust_factor = issue['avg_trust'] or 1.0

    priority = (
        i_weight * volume_factor * decay *
        recurrence_boost * trend_modifier * trust_factor
    )

    await db.execute("""
        UPDATE issues SET
            priority_score = %s,
            avg_trust_score = %s,
            updated_at = NOW()
        WHERE issue_id = %s
    """, [priority, issue['avg_trust'], issue_id])
```

### 4.3 Issue Review Drill-Down

```python
async def get_issue_reviews(issue_id: str,
                            sort_by: str = 'date',
                            limit: int = 50,
                            offset: int = 0) -> list[dict]:
    """Fetch all reviews for an issue with full details."""

    order_clause = {
        'date': 's.review_time DESC',
        'intensity': "CASE s.intensity WHEN 'I3' THEN 1 WHEN 'I2' THEN 2 ELSE 3 END",
        'trust': 're.trust_score DESC',
        'rating': 're.rating ASC'
    }.get(sort_by, 's.review_time DESC')

    return await db.query(f"""
        SELECT
            re.review_id,
            re.text,
            s.review_time,
            re.rating,
            re.valence,
            s.intensity,
            re.comparative,
            re.trust_score,
            re.quotes,
            re.staff_mentions,
            s.is_primary_match,
            s.weight,
            l.display_name as location_name
        FROM issue_spans s
        JOIN reviews_enriched re ON s.review_id = re.review_id AND re.is_latest = TRUE
        JOIN locations l ON (re.business_id, re.place_id) = (l.business_id, l.place_id)
        WHERE s.issue_id = %s
        ORDER BY {order_clause}
        LIMIT %s OFFSET %s
    """, [issue_id, limit, offset])
```

### 4.4 Strength Score

```
Strength Score = Σ (intensity_weight)

Where:
  I1 (mild)     → weight = 1
  I2 (moderate) → weight = 2
  I3 (strong)   → weight = 4

One I3 review = 4 I1 reviews = 2 I2 reviews
```

---

## Part 5: Analytics Spine (Fact Population)

### 5.1 Daily Fact Aggregation Job

```python
async def populate_facts(business_id: str, date: date, bucket_type: str = 'day'):
    """
    Aggregate reviews into fact_timeseries. Run daily.

    v3.1 populates:
      - subject_type='overall', subject_id='all' (per location + 'ALL')
      - subject_type='urt_code', subject_id=<code> (per location + 'ALL')

    Domain rollups are derived at query time from urt_code facts.
    Issue facts are optional (recommended for v3.2).
    """

    if bucket_type == 'day':
        period_start = date
        period_end = date + timedelta(days=1)
    elif bucket_type == 'week':
        period_start = date - timedelta(days=date.weekday())
        period_end = period_start + timedelta(days=7)
    elif bucket_type == 'month':
        period_start = date.replace(day=1)
        next_month = period_start.replace(day=28) + timedelta(days=4)
        period_end = next_month.replace(day=1)

    locations = await db.query(
        "SELECT place_id FROM locations WHERE business_id = %s AND is_active = TRUE",
        [business_id]
    )

    all_place_ids = [loc['place_id'] for loc in locations]

    # Per-location facts
    for loc in locations:
        place_id = loc['place_id']
        await populate_location_facts(
            business_id, place_id, period_start, period_end, bucket_type
        )

    # All-locations rollup (place_id='ALL')
    await populate_all_locations_facts(
        business_id, all_place_ids, period_start, period_end, bucket_type
    )


async def populate_location_facts(business_id: str, place_id: str,
                                   period_start: date, period_end: date,
                                   bucket_type: str):
    """Populate facts for a single location."""

    # Aggregate by URT code
    code_stats = await aggregate_by_code(
        business_id, place_id, period_start, period_end
    )

    for stat in code_stats:
        await upsert_fact(
            business_id=business_id,
            place_id=place_id,
            period_date=period_start,
            bucket_type=bucket_type,
            subject_type='urt_code',
            subject_id=stat['code'],
            metrics=stat
        )

    # Aggregate overall
    overall = await aggregate_overall(
        business_id, place_id, period_start, period_end
    )
    await upsert_fact(
        business_id=business_id,
        place_id=place_id,
        period_date=period_start,
        bucket_type=bucket_type,
        subject_type='overall',
        subject_id='all',
        metrics=overall
    )


async def populate_all_locations_facts(business_id: str, place_ids: list[str],
                                        period_start: date, period_end: date,
                                        bucket_type: str):
    """Populate 'ALL' rollup facts across all locations."""

    # Aggregate by URT code across all locations
    code_stats = await db.query("""
        SELECT
            urt_primary as code,
            COUNT(*) as review_count,
            SUM(CASE WHEN valence = 'V-' THEN 1 ELSE 0 END) as negative_count,
            SUM(CASE WHEN valence = 'V+' THEN 1 ELSE 0 END) as positive_count,
            SUM(CASE intensity
                WHEN 'I1' THEN 1 WHEN 'I2' THEN 2 WHEN 'I3' THEN 4 ELSE 0
            END) as strength_score,
            AVG(rating) as avg_rating
        FROM reviews_enriched
        WHERE business_id = %s
          AND place_id = ANY(%s)
          AND review_time >= %s AND review_time < %s
          AND is_latest = TRUE
        GROUP BY urt_primary
    """, [business_id, place_ids, period_start, period_end])

    for stat in code_stats:
        await upsert_fact(
            business_id=business_id,
            place_id='ALL',
            period_date=period_start,
            bucket_type=bucket_type,
            subject_type='urt_code',
            subject_id=stat['code'],
            metrics=stat
        )

    # Overall rollup
    overall = await db.query_one("""
        SELECT
            COUNT(*) as review_count,
            SUM(CASE WHEN valence = 'V-' THEN 1 ELSE 0 END) as negative_count,
            SUM(CASE WHEN valence = 'V+' THEN 1 ELSE 0 END) as positive_count,
            AVG(rating) as avg_rating
        FROM reviews_enriched
        WHERE business_id = %s
          AND place_id = ANY(%s)
          AND review_time >= %s AND review_time < %s
          AND is_latest = TRUE
    """, [business_id, place_ids, period_start, period_end])

    await upsert_fact(
        business_id=business_id,
        place_id='ALL',
        period_date=period_start,
        bucket_type=bucket_type,
        subject_type='overall',
        subject_id='all',
        metrics=overall
    )
```

### 5.2 Timeline Query (For Charts)

```python
async def get_timeline(business_id: str,
                       place_id: Optional[str],
                       subject_type: str,
                       subject_id: str,
                       start: date,
                       end: date,
                       bucket_type: str = 'week') -> list[dict]:
    """
    Query pre-aggregated facts for line charts.

    Args:
        place_id: Specific place_id, or None for 'ALL' locations rollup
    """

    # Use 'ALL' sentinel for all-locations queries
    effective_place_id = place_id if place_id else 'ALL'

    return await db.query("""
        SELECT
            period_date,
            review_count,
            negative_count,
            positive_count,
            strength_score,
            negative_strength,
            avg_rating,
            cr_better,
            cr_worse,
            cr_same
        FROM fact_timeseries
        WHERE business_id = %s
          AND place_id = %s
          AND subject_type = %s
          AND subject_id = %s
          AND bucket_type = %s
          AND period_date BETWEEN %s AND %s
        ORDER BY period_date
    """, [business_id, effective_place_id, subject_type, subject_id, bucket_type, start, end])
```

---

## Part 6: Competitor Analysis

### 6.1 Competitor Setup (Clean Model)

Competitors are tracked in the `competitors` table only. They are **not** injected into `locations` with fake business_ids. Competitor reviews are stored in `reviews_raw` and `reviews_enriched` with `business_id = NULL` or a dedicated `__competitors__` partition.

```python
async def setup_competitor(business_id: str, competitor_place_id: str,
                           competitor_name: str, relationship: str = 'direct'):
    """Register a competitor for tracking."""

    await db.execute("""
        INSERT INTO competitors (business_id, competitor_place_id, competitor_name, relationship)
        VALUES (%s, %s, %s, %s)
        ON CONFLICT (business_id, competitor_place_id) DO UPDATE SET
            competitor_name = EXCLUDED.competitor_name,
            relationship = EXCLUDED.relationship
    """, [business_id, competitor_place_id, competitor_name, relationship])


async def pull_competitor_reviews(business_id: str):
    """Pull reviews for all tracked competitors."""

    competitors = await db.query("""
        SELECT competitor_place_id, competitor_name
        FROM competitors
        WHERE business_id = %s AND is_active = TRUE
    """, [business_id])

    for comp in competitors:
        # Store competitor reviews with special business_id marker
        await pull_reviews_for_competitor(
            business_id=business_id,
            place_id=comp['competitor_place_id']
        )
```

### 6.2 Competitor Comparison

```python
async def get_competitor_comparison(business_id: str, code: str,
                                    start: date, end: date) -> dict:
    """Compare your URT metrics against competitors."""

    # Your metrics (from 'ALL' rollup)
    your_metrics = await db.query_one("""
        SELECT
            SUM(negative_strength) as negative_strength,
            SUM(review_count) as review_count,
            AVG(avg_rating) as avg_rating
        FROM fact_timeseries
        WHERE business_id = %s
          AND place_id = 'ALL'
          AND subject_type = 'urt_code'
          AND subject_id = %s
          AND period_date BETWEEN %s AND %s
    """, [business_id, code, start, end])

    # Competitor metrics
    competitors = await db.query("""
        SELECT competitor_place_id, competitor_name
        FROM competitors WHERE business_id = %s AND is_active = TRUE
    """, [business_id])

    comparison = {
        'your_business': your_metrics or {},
        'competitors': []
    }

    for comp in competitors:
        # Query competitor's facts (stored with their place_id)
        comp_metrics = await db.query_one("""
            SELECT
                SUM(negative_strength) as negative_strength,
                SUM(review_count) as review_count,
                AVG(avg_rating) as avg_rating
            FROM fact_timeseries
            WHERE place_id = %s
              AND subject_type = 'urt_code'
              AND subject_id = %s
              AND period_date BETWEEN %s AND %s
        """, [comp['competitor_place_id'], code, start, end])

        comparison['competitors'].append({
            'name': comp['competitor_name'],
            'place_id': comp['competitor_place_id'],
            **(comp_metrics or {})
        })

    return comparison
```

---

## Part 7: Report Generation (Facts-First)

```python
async def generate_report(business_id: str, place_id: Optional[str],
                          start: date, end: date) -> dict:
    """Generate report from pre-aggregated facts."""

    # Use 'ALL' for cross-location reports
    effective_place_id = place_id if place_id else 'ALL'

    # 1. Top issues from facts (fast)
    top_issues = await get_top_issues_from_facts(business_id, effective_place_id, start, end)

    # 2. Strengths from facts
    strengths = await get_strengths_from_facts(business_id, effective_place_id, start, end)

    # 3. Sub-patterns (store results)
    for issue in top_issues[:5]:
        patterns = await discover_and_store_subpatterns(
            business_id, effective_place_id, issue['code'], start, end
        )
        issue['sub_patterns'] = patterns

    # 4. Trends from facts
    trends = await compute_trends_from_facts(business_id, effective_place_id, start, end)

    # 5. Staff analysis
    staff = await analyze_staff(business_id, effective_place_id, start, end)

    # 6. Competitor benchmarks
    competitors = await get_competitor_benchmarks(business_id, start, end)

    payload = {
        'business_id': business_id,
        'place_id': place_id,  # Original (None = all)
        'period': f"{start} to {end}",
        'issues': top_issues,
        'strengths': strengths,
        'trends': trends,
        'staff': staff,
        'competitors': competitors,
    }

    narrative = await generate_narrative(payload)

    return {
        'payload': payload,
        'narrative': narrative,
        'generated_at': datetime.now().isoformat()
    }
```

---

## Part 8: KPI-Ready Hooks

### 8.1 Future KPI Integration (Interface Only)

```sql
-- Future: KPI fact table with same grain
CREATE TABLE fact_kpi_timeseries (
    id                  SERIAL PRIMARY KEY,

    -- Same join keys as fact_timeseries
    business_id         TEXT NOT NULL,
    place_id            TEXT NOT NULL,          -- 'ALL' for rollups
    period_date         DATE NOT NULL,
    bucket_type         TEXT NOT NULL,

    -- KPI metrics
    revenue             DECIMAL(12,2),
    transactions        INT,
    cancellations       INT,
    refunds             DECIMAL(12,2),
    support_tickets     INT,

    computed_at         TIMESTAMP DEFAULT NOW(),

    UNIQUE(business_id, place_id, period_date, bucket_type)
);

-- Join reviews and KPIs:
SELECT
    r.period_date,
    r.negative_strength,
    k.revenue,
    k.cancellations
FROM fact_timeseries r
JOIN fact_kpi_timeseries k USING (business_id, place_id, period_date, bucket_type)
WHERE r.subject_type = 'overall' AND r.subject_id = 'all';
```

---

## Part 9: Cost Model

| Stage | When | Cost | Notes |
|-------|------|------|-------|
| **Raw Storage** | Per review | $0.00 | ~1KB per review |
| **Embedding** | Per review | $0.00 | Local model, ~50ms |
| **LLM Classification** | Per review | ~$0.0002 | GPT-4o-mini |
| **Fact Aggregation** | Daily job | $0.00 | SQL, <1 minute |
| **Sub-Clustering** | Per report | $0.00 | HDBSCAN, <1s |
| **LLM Narrative** | Per report | ~$0.15 | GPT-4o |

**Total Costs:**

| Volume | Monthly Ingest | Reports (10/mo) | Total |
|--------|---------------|-----------------|-------|
| 1K reviews | $0.20 | $1.50 | **$1.70** |
| 10K reviews | $2.00 | $1.50 | **$3.50** |
| 100K reviews | $20.00 | $1.50 | **$21.50** |

---

## Part 10: Key Innovations

| Innovation | Benefit |
|------------|---------|
| **Relational issue_spans** | Scales, queryable, no array limits |
| **Versioned reviews_enriched** | Handles edited reviews correctly |
| **Tenant-scoped locations** | Same place_id works for multiple businesses |
| **place_id='ALL' sentinel** | Clean all-locations rollups without NULL handling |
| **Unified fact_timeseries spine** | Fast dashboards, KPI-joinable later |
| **Trust score weighting** | Spam resistance without deletion |
| **Competitor separate table** | Clean model, no fake business_ids |
| **Universal join keys** | `(business_id, place_id, period, bucket)` future-proofs KPI integration |

---

## Document Control

| Field | Value |
|-------|-------|
| **Document** | ReviewIQ Architecture v3.1.1 |
| **Status** | Specification Complete (Reviewed) |
| **Date** | 2026-01-24 |
| **Dependencies** | URT Specification v5.1, Issue Lifecycle Framework C1 |
| **Source** | Google Reviews only |
| **Cost Target** | <$25/month at 100K reviews |

### Changelog

| Version | Changes |
|---------|---------|
| v3.0 | Issue lifecycle, strength scores, timeline charts |
| v3.1 | Relational refactor: issue_spans, fact_timeseries, raw/enriched split, multi-location, competitors, trust scoring |
| v3.1.1 | **Reviewed**: Versioned enriched PK, tenant-scoped locations, 'ALL' sentinel, competitor cleanup, fixed get_timeline params, clarified issue key scope |

### Fixes Applied (v3.1.1)

| Issue | Fix |
|-------|-----|
| reviews_enriched PK wrong for edits | PK = `(source, review_id, review_version)` + `is_latest` flag |
| raw_id ambiguous under versioning | raw_id references specific raw version |
| locations.place_id prevents multi-tenant | PK = `(business_id, place_id)` (tenant-scoped) |
| Competitor fake business_id pattern | Removed; competitors table is separate, no injection into locations |
| fact_timeseries.place_id NOT NULL blocks rollups | `place_id='ALL'` sentinel for all-locations |
| get_timeline param ordering bug | Fixed: params built in correct order |
| Issue entity fields but no extraction | Clarified: v3.1 key is `(business_id, place_id, primary_subcode)` only; entity fields reserved for v3.2 |
| Missing indexes | Added `idx_spans_issue_time`, FK to locations |

### Deferred to v3.2+

| Feature | Reason |
|---------|--------|
| Entity extraction for issues | Needs NER pipeline |
| journey_step inference | Needs better grounding data |
| intent_signals extraction | Needs action playbooks |
| stability_score tracking | Premature for v1 |
| issue facts in fact_timeseries | Optional performance optimization |
| KPI integration | Placeholder only in v3.1 |

---

*End of ReviewIQ Architecture v3.1.1*