diff --git a/neuraldb-docs/pages/install-cloud.md b/neuraldb-docs/pages/install-cloud.md
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+---
+title: Cloud Managed
+sort: 120
+section-id: installation
+keywords: cloud, managed, NeuralDB Cloud, regions, tiers, SaaS
+description: Setting up NeuralDB Cloud — the fully managed service with global regions and flexible tiers
+language: en
+---
+
+# Cloud Managed
+
+NeuralDB Cloud is the fully managed version of NeuralDB. It handles provisioning, patching, backups, monitoring, and scaling — so you can focus on building your application rather than managing database infrastructure.
+
+## Getting Started
+
+### 1. Create an Account
+
+Sign up at [cloud.neuraldb.io](https://cloud.neuraldb.io). You can authenticate with Google, GitHub, or an email address.
+
+### 2. Create a Cluster
+
+Click **New Cluster** and configure:
+
+- **Region**: choose the cloud region closest to your application servers
+- **Tier**: select based on your workload requirements (see tier comparison below)
+- **Storage**: initial storage allocation (can be scaled later)
+- **High Availability**: enable for production workloads
+
+### 3. Connect
+
+Once the cluster is provisioned (typically under 3 minutes), your connection string appears in the dashboard:
+
+```
+postgresql://neuraldb:[password]@[cluster-id].cloud.neuraldb.io:5432/[database]?sslmode=require
+```
+
+Use this with any PostgreSQL-compatible driver or psql:
+
+```bash
+psql "postgresql://neuraldb:mypassword@abc123.cloud.neuraldb.io:5432/mydb?sslmode=require"
+```
+
+## Available Regions
+
+| Region | Cloud Provider | Availability |
+|--------|---------------|-------------|
+| us-east-1 (N. Virginia) | AWS | GA |
+| us-west-2 (Oregon) | AWS | GA |
+| eu-west-1 (Ireland) | AWS | GA |
+| eu-central-1 (Frankfurt) | AWS | GA |
+| ap-northeast-1 (Tokyo) | AWS | GA |
+| ap-southeast-1 (Singapore) | AWS | GA |
+| us-central1 (Iowa) | GCP | Beta |
+| europe-west4 (Netherlands) | GCP | Beta |
+| eastus (Virginia) | Azure | Beta |
+
+Multi-region replication is available on Business and Enterprise tiers.
+
+## Pricing Tiers
+
+### Starter
+
+Free tier for development and experimentation.
+
+| Resource | Limit |
+|---------|-------|
+| Storage | 5 GB |
+| Vector dimensions | Up to 1536 |
+| Max connections | 10 |
+| PITR | No |
+| HA | No |
+| SLA | No |
+
+### Developer
+
+$29/month.
+
+| Resource | Limit |
+|---------|-------|
+| vCPU | 2 dedicated |
+| RAM | 8 GB |
+| Storage | 100 GB NVMe SSD |
+| Connections | 100 |
+| PITR | 7 days |
+| HA | No |
+
+### Business
+
+$199/month.
+
+| Resource | Limit |
+|---------|-------|
+| vCPU | 8 dedicated |
+| RAM | 32 GB |
+| Storage | 500 GB NVMe SSD |
+| Connections | 500 |
+| PITR | 30 days |
+| HA | Yes (1 standby) |
+| Read replicas | Up to 3 |
+| SLA | 99.95% |
+
+### Enterprise
+
+Custom pricing for mission-critical applications.
+
+## Connecting from Your Application
+
+### Connection Pooling
+
+NeuralDB Cloud includes PgBouncer-based connection pooling:
+
+```
+postgresql://neuraldb:[password]@[cluster-id]-pooler.cloud.neuraldb.io:5432/[database]
+```
+
+### SSL/TLS
+
+All connections require TLS. Download the cluster CA certificate from the dashboard:
+
+```
+sslmode=verify-full&sslrootcert=/path/to/ca.pem
+```
+
+## Branching
+
+Create instant copy-on-write clones of your production database:
+
+```bash
+neuraldb-cloud branch create staging --from production
+```
diff --git a/neuraldb-docs/pages/install-docker.md b/neuraldb-docs/pages/install-docker.md
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+---
+title: Docker Install
+sort: 100
+section-id: installation
+keywords: Docker, install, docker run, docker-compose, volumes, container
+description: Installing NeuralDB using Docker — single container and docker-compose setups
+language: en
+---
+
+# Docker Install
+
+Docker is the fastest way to run NeuralDB locally or in a single-server deployment.
+
+## Quick Start
+
+```bash
+docker run -d \
+  --name neuraldb \
+  -p 5432:5432 \
+  -e NEURALDB_PASSWORD=mypassword \
+  -e NEURALDB_DB=mydb \
+  -v neuraldb_data:/var/lib/neuraldb/data \
+  neuraldb/neuraldb:latest
+```
+
+Connect with psql:
+
+```bash
+psql -h localhost -p 5432 -U neuraldb -d mydb
+```
+
+## Environment Variables
+
+| Variable | Default | Description |
+|----------|---------|-------------|
+| `NEURALDB_PASSWORD` | required | Password for the `neuraldb` superuser |
+| `NEURALDB_USER` | `neuraldb` | Superuser username |
+| `NEURALDB_DB` | `neuraldb` | Default database name |
+| `NEURALDB_PORT` | `5432` | TCP port |
+| `NEURALDB_SHARED_BUFFERS` | `256MB` | Row store page cache |
+| `NEURALDB_VECTOR_BUFFER` | `512MB` | Vector index memory |
+
+## docker-compose Setup
+
+```yaml
+version: '3.9'
+
+services:
+  neuraldb:
+    image: neuraldb/neuraldb:1.0
+    container_name: neuraldb
+    restart: unless-stopped
+    ports:
+      - "127.0.0.1:5432:5432"
+    environment:
+      NEURALDB_PASSWORD: ${NEURALDB_PASSWORD}
+      NEURALDB_SHARED_BUFFERS: "4GB"
+      NEURALDB_VECTOR_BUFFER: "8GB"
+    volumes:
+      - neuraldb_data:/var/lib/neuraldb/data
+    healthcheck:
+      test: ["CMD-SHELL", "pg_isready -U neuraldb"]
+      interval: 10s
+      timeout: 5s
+      retries: 5
+
+volumes:
+  neuraldb_data:
+```
+
+```bash
+echo "NEURALDB_PASSWORD=$(openssl rand -base64 32)" > .env
+docker-compose up -d
+```
+
+## Upgrading
+
+```bash
+docker pull neuraldb/neuraldb:1.1
+docker stop neuraldb && docker rm neuraldb
+docker run -d --name neuraldb \
+  -v neuraldb_data:/var/lib/neuraldb/data \
+  -e NEURALDB_PASSWORD=mypassword \
+  neuraldb/neuraldb:1.1
+docker exec neuraldb neuraldb-migrate
+```
diff --git a/neuraldb-docs/pages/install-kubernetes.md b/neuraldb-docs/pages/install-kubernetes.md
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+---
+title: Kubernetes
+sort: 110
+section-id: installation
+keywords: Kubernetes, Helm, StatefulSet, PVC, k8s, cluster, deployment
+description: Deploying NeuralDB on Kubernetes using the official Helm chart and StatefulSets
+language: en
+---
+
+# Kubernetes
+
+The recommended way to run NeuralDB on Kubernetes is via the official Helm chart.
+
+## Installing the Helm Chart
+
+```bash
+helm repo add neuraldb https://charts.neuraldb.io
+helm repo update
+kubectl create namespace neuraldb
+helm install neuraldb neuraldb/neuraldb \
+  --namespace neuraldb \
+  --set auth.password=mysecretpassword \
+  --set persistence.size=100Gi
+```
+
+## Chart Configuration
+
+```yaml
+image:
+  repository: neuraldb/neuraldb
+  tag: "1.0"
+
+replicaCount: 1
+readReplicaCount: 2
+
+resources:
+  requests:
+    cpu: "2"
+    memory: "8Gi"
+  limits:
+    cpu: "8"
+    memory: "32Gi"
+
+persistence:
+  enabled: true
+  storageClass: "fast-ssd"
+  size: 500Gi
+
+vectorBuffer: "16Gi"
+sharedBuffers: "8Gi"
+maxConnections: 200
+
+ha:
+  enabled: true
+  replication:
+    mode: synchronous
+
+backup:
+  enabled: true
+  schedule: "0 2 * * *"
+  s3:
+    bucket: my-neuraldb-backups
+    region: us-east-1
+```
+
+## Services
+
+| Service | Port | Description |
+|---------|------|-------------|
+| `neuraldb-primary` | 5432 | Primary — reads + writes |
+| `neuraldb-replica` | 5432 | Read replicas — reads only |
+| `neuraldb-headless` | 5432 | StatefulSet pod discovery |
+
+## Scaling Read Replicas
+
+```bash
+helm upgrade neuraldb neuraldb/neuraldb \
+  --namespace neuraldb \
+  --set readReplicaCount=4
+```
diff --git a/neuraldb-docs/pages/install-local.md b/neuraldb-docs/pages/install-local.md
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+---
+title: Local Development
+sort: 130
+section-id: installation
+keywords: local, development, binary, homebrew, winget, install, macOS, Linux, Windows
+description: Installing NeuralDB locally for development using binaries, Homebrew, or winget
+language: en
+---
+
+# Local Development
+
+## macOS
+
+```bash
+brew tap neuraldb/tap
+brew install neuraldb
+brew services start neuraldb
+```
+
+## Linux
+
+### Ubuntu / Debian
+
+```bash
+curl -fsSL https://packages.neuraldb.io/gpg | sudo gpg --dearmor -o /usr/share/keyrings/neuraldb-keyring.gpg
+echo "deb [signed-by=/usr/share/keyrings/neuraldb-keyring.gpg] https://packages.neuraldb.io/apt stable main" \
+  | sudo tee /etc/apt/sources.list.d/neuraldb.list
+sudo apt update && sudo apt install -y neuraldb
+sudo systemctl enable --now neuraldb
+```
+
+### RHEL / Fedora
+
+```bash
+sudo rpm --import https://packages.neuraldb.io/gpg
+sudo tee /etc/yum.repos.d/neuraldb.repo <<'EOF'
+[neuraldb]
+name=NeuralDB Repository
+baseurl=https://packages.neuraldb.io/rpm/stable
+enabled=1
+gpgcheck=1
+gpgkey=https://packages.neuraldb.io/gpg
+EOF
+sudo dnf install -y neuraldb
+sudo systemctl enable --now neuraldb
+```
+
+## Windows
+
+```powershell
+winget install NeuralDB.NeuralDB
+```
+
+## First-Time Setup
+
+```bash
+neuraldb init
+neuraldb start
+neuraldb-cli
+```
+
+```sql
+ALTER USER neuraldb PASSWORD 'your-new-password';
+CREATE DATABASE myapp;
+```
diff --git a/neuraldb-docs/pages/nql-aggregations.md b/neuraldb-docs/pages/nql-aggregations.md
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+---
+title: Aggregations
+sort: 130
+section-id: query-language
+keywords: aggregations, GROUP BY, COUNT, SUM, vectors, AVG, centroid, analytics
+description: Aggregating data in NQL including GROUP BY, COUNT, SUM, and vector-specific aggregation functions
+language: en
+---
+
+# Aggregations
+
+NQL supports the full SQL aggregation toolkit, extended with vector-specific aggregate functions.
+
+## Standard Aggregations
+
+```sql
+SELECT category, COUNT(*) AS doc_count
+FROM documents
+GROUP BY category
+ORDER BY doc_count DESC;
+
+SELECT category, AVG(price), MIN(price), MAX(price)
+FROM products
+WHERE available = true
+GROUP BY category;
+```
+
+## Vector Aggregations
+
+### `AVG(embedding)` — Centroid
+
+```sql
+SELECT AVG(embedding) AS centroid
+FROM documents
+WHERE category = 'technology';
+```
+
+Find documents closest to the centroid:
+
+```sql
+WITH centroid AS (
+  SELECT AVG(embedding) AS c FROM documents WHERE category = 'technology'
+)
+SELECT id, title, 1 - (embedding <=> centroid.c) AS similarity
+FROM documents, centroid
+WHERE category = 'technology'
+ORDER BY embedding <=> centroid.c
+LIMIT 10;
+```
+
+### `vector_centroid(embedding, weight)`
+
+```sql
+SELECT vector_centroid(embedding, rating) AS weighted_centroid
+FROM products WHERE category = 'electronics';
+```
+
+## GROUP BY with Vector Search
+
+```sql
+SELECT DISTINCT ON (category)
+  id, category, title, 1 - (embedding <=> $1) AS similarity
+FROM documents
+ORDER BY category, embedding <=> $1;
+```
+
+## Window Functions
+
+```sql
+SELECT id, title, category,
+  1 - (embedding <=> $1) AS similarity,
+  RANK() OVER (PARTITION BY category ORDER BY embedding <=> $1) AS rank_in_category
+FROM documents
+WHERE 1 - (embedding <=> $1) > 0.5
+ORDER BY category, rank_in_category;
+```
+
+## Time-Series Semantic Analytics
+
+```sql
+WITH weekly_centroids AS (
+  SELECT date_trunc('week', created_at) AS week, AVG(embedding) AS centroid
+  FROM documents GROUP BY week
+)
+SELECT w1.week, 1 - (w1.centroid <=> w2.centroid) AS similarity_to_prev_week
+FROM weekly_centroids w1
+LEFT JOIN weekly_centroids w2 ON w2.week = w1.week - INTERVAL '1 week'
+ORDER BY w1.week;
+```
diff --git a/neuraldb-docs/pages/nql-basics.md b/neuraldb-docs/pages/nql-basics.md
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+---
+title: NQL Basics
+sort: 100
+section-id: query-language
+keywords: NQL, NeuralDB Query Language, SQL, syntax, basics, queries
+description: Introduction to NeuralDB Query Language (NQL) — syntax, data types, and basic operations
+language: en
+---
+
+# NQL Basics
+
+NQL (NeuralDB Query Language) is a superset of standard SQL. Every valid SQL statement is also valid NQL. NQL adds extensions for vector operations, embedding generation, and semantic search primitives.
+
+## Connecting
+
+```bash
+psql -h localhost -p 5432 -U neuraldb -d mydb
+neuraldb-cli -h localhost
+```
+
+## Data Types
+
+### VECTOR(n)
+
+```sql
+CREATE TABLE documents (
+  id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
+  content TEXT NOT NULL,
+  embedding VECTOR(1536)
+);
+```
+
+### HALFVEC(n) and SPARSEVEC(n)
+
+```sql
+embedding HALFVEC(1536)   -- 16-bit, half the memory
+bm25_vector SPARSEVEC(30000)  -- sparse, non-zero elements only
+```
+
+## Basic CRUD
+
+```sql
+CREATE TABLE products (
+  id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
+  name TEXT NOT NULL, category TEXT, price DECIMAL(10,2),
+  stock INTEGER DEFAULT 0, embedding VECTOR(1536),
+  created_at TIMESTAMP WITH TIME ZONE DEFAULT NOW()
+);
+
+INSERT INTO products (name, category, price, stock, embedding)
+VALUES ('Wireless Headphones', 'electronics', 299.99, 150, '[0.023, -0.187, ...]');
+
+SELECT id, name, price FROM products WHERE category = 'electronics';
+
+UPDATE products SET price = 279.99, embedding = '[...]' WHERE id = $1;
+
+DELETE FROM products WHERE id = $1;
+```
+
+## Creating Vector Indexes
+
+```sql
+CREATE INDEX ON documents USING hnsw (embedding vector_cosine_ops);
+CREATE INDEX ON documents USING hnsw (embedding vector_l2_ops);
+CREATE INDEX ON documents USING hnsw (embedding vector_ip_ops);
+```
+
+## Basic Vector Queries
+
+```sql
+SELECT id, content, 1 - (embedding <=> $1) AS similarity
+FROM documents
+ORDER BY embedding <=> $1
+LIMIT 10;
+```
+
+| Operator | Metric | Index ops |
+|----------|--------|----------|
+| `<=>` | Cosine distance | `vector_cosine_ops` |
+| `<->` | Euclidean (L2) | `vector_l2_ops` |
+| `<#>` | Negative dot product | `vector_ip_ops` |
+
+## NQL Functions
+
+```sql
+SELECT vector_dims(embedding) FROM documents LIMIT 1;  -- returns 1536
+SELECT vector_norm(embedding) FROM documents LIMIT 5;
+SELECT cosine_similarity(embedding, $1) AS similarity FROM documents ORDER BY similarity DESC LIMIT 10;
+```
diff --git a/neuraldb-docs/pages/nql-hybrid.md b/neuraldb-docs/pages/nql-hybrid.md
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+---
+title: Hybrid Queries
+sort: 120
+section-id: query-language
+keywords: hybrid queries, vector, relational, filters, combined, semantic search, metadata
+description: Combining vector similarity and relational filters in NQL hybrid queries
+language: en
+---
+
+# Hybrid Queries
+
+Hybrid queries combine vector similarity search with relational filter predicates in a single SQL statement.
+
+## Basic Hybrid Query
+
+```sql
+SELECT id, name, price, 1 - (embedding <=> $1) AS similarity
+FROM products
+WHERE category = 'electronics'
+  AND stock > 0
+  AND price < 500
+ORDER BY embedding <=> $1
+LIMIT 10;
+```
+
+## Query Planner Hints
+
+```sql
+-- Force pre-filter
+SELECT /*+ PREFILTER */ id, name, 1 - (embedding <=> $1) AS score
+FROM products WHERE category = 'electronics'
+ORDER BY score DESC LIMIT 10;
+
+-- Force post-filter
+SELECT /*+ POSTFILTER */ id, name, 1 - (embedding <=> $1) AS score
+FROM products WHERE price < 500
+ORDER BY embedding <=> $1 LIMIT 10;
+```
+
+## Hybrid Full-Text + Vector (BM25)
+
+```sql
+WITH vector_search AS (
+  SELECT id, ROW_NUMBER() OVER (ORDER BY embedding <=> $1) AS rank
+  FROM documents ORDER BY embedding <=> $1 LIMIT 100
+),
+fts_search AS (
+  SELECT id, ROW_NUMBER() OVER (ORDER BY ts_rank_cd(tsv, query) DESC) AS rank
+  FROM documents, to_tsquery('english', $2) query
+  WHERE tsv @@ query ORDER BY ts_rank_cd(tsv, query) DESC LIMIT 100
+),
+rrf AS (
+  SELECT COALESCE(v.id, f.id) AS id,
+    (COALESCE(1.0/(60+v.rank),0) + COALESCE(1.0/(60+f.rank),0)) AS rrf_score
+  FROM vector_search v FULL OUTER JOIN fts_search f ON v.id = f.id
+)
+SELECT d.id, d.content, rrf.rrf_score
+FROM rrf JOIN documents d ON d.id = rrf.id
+ORDER BY rrf_score DESC LIMIT 10;
+```
+
+## Composite Scoring
+
+```sql
+SELECT id, name, price, rating,
+  (0.7 * (1 - (embedding <=> $1))
+   + 0.2 * (rating / 5.0)
+   + 0.1 * (1 - EXTRACT(DAYS FROM NOW() - created_at) / 365.0)
+  ) AS composite_score
+FROM products
+WHERE available = true AND price < $2
+ORDER BY composite_score DESC LIMIT 20;
+```
diff --git a/neuraldb-docs/pages/nql-transactions.md b/neuraldb-docs/pages/nql-transactions.md
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+---
+title: Transactions
+sort: 140
+section-id: query-language
+keywords: transactions, ACID, isolation levels, MVCC, BEGIN, COMMIT, ROLLBACK
+description: ACID transactions in NeuralDB — isolation levels, MVCC, savepoints, and advisory locks
+language: en
+---
+
+# Transactions
+
+NeuralDB provides full ACID transactions with MVCC. Unlike most vector databases, NeuralDB guarantees atomicity across both relational and vector data.
+
+## Basic Transaction Syntax
+
+```sql
+BEGIN;
+INSERT INTO documents (content, embedding) VALUES ($1, $2);
+UPDATE document_stats SET total_count = total_count + 1;
+COMMIT;
+```
+
+## Isolation Levels
+
+```sql
+BEGIN;
+SET TRANSACTION ISOLATION LEVEL READ COMMITTED;
+-- each statement sees only rows committed before it
+COMMIT;
+
+BEGIN ISOLATION LEVEL REPEATABLE READ;
+-- reads are stable throughout the transaction
+COMMIT;
+
+BEGIN ISOLATION LEVEL SERIALIZABLE;
+-- may raise: ERROR: could not serialize access
+COMMIT;
+```
+
+## Savepoints
+
+```sql
+BEGIN;
+INSERT INTO documents (content, embedding) VALUES ($1, $2);
+SAVEPOINT after_insert;
+UPDATE document_stats SET count = count + 1 WHERE id = $3;
+ROLLBACK TO SAVEPOINT after_insert;
+UPDATE document_stats SET count = count + 1 WHERE id = $4;
+COMMIT;
+```
+
+## Vector Transactions
+
+```sql
+BEGIN;
+INSERT INTO documents (id, content, embedding) VALUES ($1, $2, $3);
+-- If ROLLBACK, neither row nor index entry exists
+ROLLBACK;
+```
+
+## Advisory Locks
+
+```sql
+SELECT pg_advisory_lock(42);
+SELECT pg_try_advisory_lock(42);  -- returns boolean
+SELECT pg_advisory_unlock(42);
+SELECT pg_advisory_xact_lock(42); -- auto-released at commit/rollback
+```
diff --git a/neuraldb-docs/pages/nql-vectors.md b/neuraldb-docs/pages/nql-vectors.md
new file mode 100644
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+++ b/neuraldb-docs/pages/nql-vectors.md
@@ -0,0 +1,79 @@
+---
+title: Vector Queries
+sort: 110
+section-id: query-language
+keywords: vector queries, NEAREST, SIMILAR, cosine, dot product, euclidean, ANN
+description: Writing vector similarity queries in NQL — NEAREST, SIMILAR, distance operators, and recall tuning
+language: en
+---
+
+# Vector Queries
+
+## Distance Operators
+
+```sql
+embedding <=> query_vector  -- cosine distance
+embedding <-> query_vector  -- euclidean (L2)
+embedding <#> query_vector  -- negative dot product
+```
+
+Always pair `ORDER BY` with `LIMIT` to use the HNSW index:
+
+```sql
+SELECT id, content FROM documents
+ORDER BY embedding <=> '[0.1, 0.2, ...]'
+LIMIT 10;
+```
+
+## NEAREST Clause
+
+```sql
+SELECT id, content, score
+FROM documents
+NEAREST TO embedding = '[0.1, 0.2, ...]' USING COSINE
+TOP 10;
+```
+
+## SIMILAR Clause
+
+```sql
+SELECT id, content, score
+FROM documents
+SIMILAR TO embedding = $1 USING COSINE THRESHOLD 0.75
+LIMIT 100;
+```
+
+## Recall Tuning
+
+```sql
+SET hnsw.ef_search = 200;  -- higher = better recall, slower
+```
+
+| ef_search | Recall@10 | p50 latency | QPS |
+|-----------|-----------|-------------|-----|
+| 20 | 89% | 0.7ms | 12,000 |
+| 40 | 95% | 1.2ms | 8,400 |
+| 80 | 98% | 2.1ms | 4,800 |
+| 200 | 99.5% | 4.8ms | 2,100 |
+
+## Exact Search
+
+```sql
+SET neuraldb.vector_scan = 'exact';
+SELECT * FROM documents ORDER BY embedding <=> $1 LIMIT 10;
+RESET neuraldb.vector_scan;
+```
+
+## Multi-Vector Queries
+
+```sql
+WITH queries AS (
+  SELECT UNNEST(ARRAY['[...]'::VECTOR(1536), '[...]'::VECTOR(1536)]) AS qv
+),
+ranked AS (
+  SELECT d.id, d.content, MIN(d.embedding <=> q.qv) AS best_distance
+  FROM documents d, queries q
+  GROUP BY d.id, d.content
+)
+SELECT * FROM ranked ORDER BY best_distance LIMIT 20;
+```
diff --git a/neuraldb-docs/pages/ops-backup.md b/neuraldb-docs/pages/ops-backup.md
new file mode 100644
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--- /dev/null
+++ b/neuraldb-docs/pages/ops-backup.md
@@ -0,0 +1,70 @@
+---
+title: Backup & Restore
+sort: 110
+section-id: operations
+keywords: backup, restore, snapshot, WAL archiving, PITR, point-in-time recovery
+description: Backup and restore strategies for NeuralDB — snapshots, WAL archiving, and point-in-time recovery
+language: en
+---
+
+# Backup & Restore
+
+## Physical Snapshot
+
+```bash
+pg_basebackup \
+  --host=localhost --port=5432 --username=backup_user \
+  --pgdata=/backups/neuraldb/$(date +%Y%m%d) \
+  --wal-method=stream --checkpoint=fast --compress=lz4 --progress
+```
+
+## WAL Archiving
+
+```ini
+wal_level = replica
+archive_mode = on
+archive_command = 'aws s3 cp %p s3://my-backups/neuraldb/wal/%f'
+archive_timeout = 60
+```
+
+Verify:
+
+```sql
+SELECT last_archived_wal, last_archived_time, archived_count, failed_count
+FROM pg_stat_archiver;
+```
+
+## pgBackRest
+
+```bash
+sudo apt install pgbackrest
+# Full backup
+sudo -u postgres pgbackrest --stanza=neuraldb backup --type=full
+# Differential
+sudo -u postgres pgbackrest --stanza=neuraldb backup --type=diff
+```
+
+Cron schedule:
+
+```cron
+0 1 * * 0   postgres pgbackrest --stanza=neuraldb backup --type=full
+0 1 * * 1-6 postgres pgbackrest --stanza=neuraldb backup --type=diff
+```
+
+## Point-in-Time Recovery
+
+```bash
+systemctl stop neuraldb
+pgbackrest --stanza=neuraldb restore \
+  --target="2026-05-15 14:30:00+00" \
+  --target-action=promote --delta
+systemctl start neuraldb
+```
+
+## Logical Backup
+
+```bash
+pg_dump -h localhost -U neuraldb mydb | lz4 | \
+  aws s3 cp - s3://my-backups/neuraldb/logical-$(date +%Y%m%d).sql.lz4
+pg_dump -Fc -h localhost -U neuraldb mydb > mydb-$(date +%Y%m%d).dump
+```
diff --git a/neuraldb-docs/pages/ops-migration.md b/neuraldb-docs/pages/ops-migration.md
new file mode 100644
index 0000000..0b06a70
--- /dev/null
+++ b/neuraldb-docs/pages/ops-migration.md
@@ -0,0 +1,67 @@
+---
+title: Migration
+sort: 130
+section-id: operations
+keywords: migration, import, Postgres, Pinecone, Weaviate, data migration, ETL
+description: Migrating data to NeuralDB from PostgreSQL, Pinecone, Weaviate, and other sources
+language: en
+---
+
+# Migration
+
+## From PostgreSQL
+
+```bash
+pg_dump -h source-host -U source-user -d source-db --format=custom > source-backup.dump
+psql -h neuraldb-host -U neuraldb -c "CREATE DATABASE myapp;"
+pg_restore -h neuraldb-host -U neuraldb -d myapp --jobs=8 --no-owner source-backup.dump
+```
+
+Add vector columns post-migration:
+
+```sql
+ALTER TABLE documents ADD COLUMN embedding VECTOR(1536);
+CREATE INDEX CONCURRENTLY documents_embedding_idx
+  ON documents USING hnsw (embedding vector_cosine_ops);
+```
+
+## From PostgreSQL + pgvector
+
+```bash
+pg_dump -h source-host -U source-user -d source-db --format=custom \
+  --exclude-extension=vector > pgvector-backup.dump
+pg_restore -h neuraldb-host -U neuraldb -d myapp --jobs=8 pgvector-backup.dump
+```
+
+## From Pinecone
+
+```python
+import pinecone
+from neuraldb import NeuralDB, BulkIngestor
+
+pc = pinecone.Pinecone(api_key=os.environ["PINECONE_API_KEY"])
+index = pc.Index("my-index")
+client = NeuralDB(os.environ["NEURALDB_URL"])
+
+client.execute("""
+    CREATE TABLE IF NOT EXISTS pinecone_migration (
+        id TEXT PRIMARY KEY, embedding VECTOR(1536), metadata JSONB,
+        migrated_at TIMESTAMP WITH TIME ZONE DEFAULT NOW()
+    )
+""")
+
+ingestor = BulkIngestor(client, table="pinecone_migration", batch_size=500)
+with ingestor as ing:
+    for ids_batch in paginate_pinecone_ids(index, batch_size=1000):
+        fetch_response = index.fetch(ids=ids_batch)
+        for vector_id, vector_data in fetch_response.vectors.items():
+            ing.add({"id": vector_id, "embedding": vector_data.values, "metadata": vector_data.metadata or {}})
+```
+
+## Verifying Migration
+
+```sql
+SELECT COUNT(*) FROM documents;
+SELECT COUNT(*) FROM documents WHERE embedding IS NULL;
+SELECT index_name, hnsw_in_memory, estimated_recall FROM neuraldb_stat_vector_indexes;
+```
diff --git a/neuraldb-docs/pages/ops-monitoring.md b/neuraldb-docs/pages/ops-monitoring.md
new file mode 100644
index 0000000..838c470
--- /dev/null
+++ b/neuraldb-docs/pages/ops-monitoring.md
@@ -0,0 +1,65 @@
+---
+title: Monitoring
+sort: 100
+section-id: operations
+keywords: monitoring, Prometheus, Grafana, metrics, alerts, observability, dashboards
+description: Monitoring NeuralDB with Prometheus metrics, Grafana dashboards, and alert configuration
+language: en
+---
+
+# Monitoring
+
+## Prometheus Metrics
+
+Enable the metrics exporter:
+
+```ini
+metrics.enabled = true
+metrics.port = 9187
+metrics.path = /metrics
+```
+
+Key metrics:
+
+| Metric | Type | Description |
+|--------|------|-------------|
+| `neuraldb_connections_total` | Gauge | Current connections by state |
+| `neuraldb_query_duration_seconds` | Histogram | Query duration percentiles |
+| `neuraldb_vector_queries_total` | Counter | Vector similarity queries by index |
+| `neuraldb_hnsw_index_size_bytes` | Gauge | In-memory size of HNSW graphs |
+| `neuraldb_replication_lag_seconds` | Gauge | Time lag per replica |
+| `neuraldb_database_size_bytes` | Gauge | Total database size |
+
+## Grafana Dashboard
+
+Import official dashboard ID **18921** from Grafana.com.
+
+## Alerting Rules
+
+```yaml
+groups:
+  - name: neuraldb
+    rules:
+      - alert: NeuralDBConnectionsHigh
+        expr: neuraldb_connections_total{state="active"} / neuraldb_connections_max > 0.85
+        for: 2m
+        labels: { severity: warning }
+      - alert: NeuralDBReplicationLagHigh
+        expr: neuraldb_replication_lag_seconds > 30
+        for: 1m
+        labels: { severity: warning }
+      - alert: NeuralDBVectorBufferExhausted
+        expr: neuraldb_hnsw_index_size_bytes > (neuraldb_vector_buffer_size_bytes * 0.90)
+        for: 5m
+        labels: { severity: warning }
+```
+
+## Built-In Query Statistics
+
+```sql
+SELECT query, calls, round(mean_exec_time::numeric, 2) AS avg_ms
+FROM pg_stat_statements ORDER BY mean_exec_time DESC LIMIT 10;
+
+SELECT sum(blks_hit) * 100.0 / sum(blks_hit + blks_read) AS cache_hit_ratio
+FROM pg_stat_database WHERE datname != 'template0';
+```
diff --git a/neuraldb-docs/pages/ops-scaling.md b/neuraldb-docs/pages/ops-scaling.md
new file mode 100644
index 0000000..c2d4e06
--- /dev/null
+++ b/neuraldb-docs/pages/ops-scaling.md
@@ -0,0 +1,58 @@
+---
+title: Scaling
+sort: 120
+section-id: operations
+keywords: scaling, sharding, read replicas, horizontal scaling, capacity planning, performance
+description: Scaling NeuralDB horizontally with sharding, read replicas, and capacity planning
+language: en
+---
+
+# Scaling
+
+## Read Replicas
+
+```python
+primary = NeuralDB("postgresql://neuraldb:pass@primary:5432/mydb")
+replica = NeuralDB("postgresql://neuraldb:pass@replica:5432/mydb")
+
+def search(query_vector):
+    return replica.query("SELECT * FROM docs ORDER BY embedding <=> %s LIMIT 10", [query_vector])
+
+def insert(content, embedding):
+    return primary.execute("INSERT INTO docs (content, embedding) VALUES (%s, %s)", [content, embedding])
+```
+
+| Replicas | Approx peak QPS (1536-dim, 10M vectors) |
+|---------|-----------------------------------------|
+| 1 primary | 8,000 |
+| 1 primary + 2 replicas | 24,000 |
+| 1 primary + 4 replicas | 48,000 |
+
+## Horizontal Sharding
+
+```sql
+SELECT neuraldb_cluster.init_cluster(shards => 8, replication_factor => 2);
+
+CREATE TABLE documents (
+  id UUID NOT NULL DEFAULT gen_random_uuid(),
+  tenant_id UUID NOT NULL,
+  content TEXT,
+  embedding VECTOR(1536)
+) SHARD BY tenant_id;
+```
+
+## Capacity Planning
+
+```
+Row data    ≈ avg_row_bytes × num_rows × 1.3
+Vector data ≈ dimensions × 4 bytes × num_vectors
+HNSW graph  ≈ vector_data × 1.3  (must fit in vector_buffer)
+WAL         ≈ daily_writes × retention_days
+```
+
+| Resource | Warning | Critical |
+|---------|---------|----------|
+| Connections | 80% of max | 95% of max |
+| Storage | 70% full | 85% full |
+| vector_buffer | 80% | 90% |
+| Replication lag | 30s | 120s |
diff --git a/neuraldb-docs/pages/ops-troubleshooting.md b/neuraldb-docs/pages/ops-troubleshooting.md
new file mode 100644
index 0000000..7bdc4ce
--- /dev/null
+++ b/neuraldb-docs/pages/ops-troubleshooting.md
@@ -0,0 +1,71 @@
+---
+title: Troubleshooting
+sort: 140
+section-id: operations
+keywords: troubleshooting, errors, diagnostics, FAQ, common problems, debug
+description: Common NeuralDB errors, diagnostic techniques, and frequently asked questions
+language: en
+---
+
+# Troubleshooting
+
+## Connection Issues
+
+### `FATAL: password authentication failed`
+
+```bash
+sudo -u neuraldb neuraldb-cli
+```
+```sql
+ALTER USER neuraldb PASSWORD 'new-password';
+```
+
+### `could not connect to server: Connection refused`
+
+```bash
+systemctl status neuraldb
+ss -tlnp | grep 5432
+journalctl -u neuraldb -n 50
+```
+
+### Connection slots exhausted
+
+```sql
+SELECT count(*), state FROM pg_stat_activity GROUP BY state;
+SELECT pg_terminate_backend(pid) FROM pg_stat_activity
+WHERE state = 'idle' AND state_change < NOW() - INTERVAL '10 minutes';
+```
+
+## Vector Query Issues
+
+### Slow Vector Searches
+
+```sql
+EXPLAIN (ANALYZE, BUFFERS)
+SELECT id FROM documents ORDER BY embedding <=> '[...]' LIMIT 10;
+```
+
+Common causes: missing LIMIT, HNSW graph not in memory, ef_search too low.
+
+```sql
+SELECT * FROM neuraldb_stat_vector_indexes;  -- check hnsw_in_memory
+SET enable_seqscan = off;  -- force index for debugging
+```
+
+### Low Recall
+
+```sql
+SET hnsw.ef_search = 200;
+SET neuraldb.vector_scan = 'exact';  -- compare against exact search
+```
+
+## FAQ
+
+**Q: Can I use NeuralDB as a drop-in for PostgreSQL?**
+Yes. NeuralDB implements the PostgreSQL wire protocol.
+
+**Q: What should `vector_buffer` be set to?**
+`SELECT SUM(hnsw_graph_size_bytes) FROM neuraldb_stat_vector_indexes` — set `vector_buffer` at least this large.
+
+**Q: Is NeuralDB compatible with pgvector?**
+Yes. All pgvector types (`VECTOR`, `HALFVEC`, `SPARSEVEC`) and operators (`<=>`, `<->`, `<#>`) work without modification.
diff --git a/neuraldb-docs/pages/sdk-go.md b/neuraldb-docs/pages/sdk-go.md
new file mode 100644
index 0000000..f07c236
--- /dev/null
+++ b/neuraldb-docs/pages/sdk-go.md
@@ -0,0 +1,85 @@
+---
+title: Go SDK
+sort: 120
+section-id: client-sdks
+keywords: Go, Golang, SDK, client, connection pool, query builder, pgx
+description: The NeuralDB Go SDK — installation, connection pooling, and vector query builder
+language: en
+---
+
+# Go SDK
+
+Built on `pgx`, the high-performance PostgreSQL driver for Go.
+
+## Installation
+
+```bash
+go get github.com/neuraldb/neuraldb-go
+```
+
+Requires Go 1.21+.
+
+## Connecting
+
+```go
+client, err := neuraldb.Connect(ctx, "postgresql://neuraldb:password@localhost:5432/mydb")
+if err != nil { log.Fatal(err) }
+defer client.Close(ctx)
+```
+
+### Connection Pool
+
+```go
+config, _ := pgxpool.ParseConfig(os.Getenv("NEURALDB_URL"))
+config.MaxConns = 20
+config.MinConns = 5
+pool, _ := neuraldb.NewPool(ctx, config)
+```
+
+## Working with Vectors
+
+```go
+v := types.NewVector([]float32{0.023, -0.187, 0.412})
+
+func InsertDocument(ctx context.Context, pool *neuraldb.Pool, doc Document) error {
+    _, err := pool.Exec(ctx,
+        `INSERT INTO documents (id, content, embedding) VALUES ($1, $2, $3)`,
+        doc.ID, doc.Content, doc.Embedding,
+    )
+    return err
+}
+
+func SemanticSearch(ctx context.Context, pool *neuraldb.Pool, queryEmbedding []float32, limit int) ([]SearchResult, error) {
+    qv := types.NewVector(queryEmbedding)
+    rows, err := pool.Query(ctx, `
+        SELECT id, content, 1 - (embedding <=> $1) AS similarity
+        FROM documents WHERE embedding IS NOT NULL
+        ORDER BY embedding <=> $1 LIMIT $2
+    `, qv, limit)
+    if err != nil { return nil, err }
+    defer rows.Close()
+    var results []SearchResult
+    for rows.Next() {
+        var r SearchResult
+        rows.Scan(&r.ID, &r.Content, &r.Similarity)
+        results = append(results, r)
+    }
+    return results, rows.Err()
+}
+```
+
+## Transactions
+
+```go
+pool.BeginTxFunc(ctx, pgx.TxOptions{}, func(tx pgx.Tx) error {
+    for _, doc := range docs {
+        _, err := tx.Exec(ctx,
+            `INSERT INTO documents (content, embedding) VALUES ($1, $2)`,
+            doc.Content, doc.Embedding,
+        )
+        if err != nil { return err }
+    }
+    _, err := tx.Exec(ctx, `UPDATE stats SET doc_count = doc_count + $1`, len(docs))
+    return err
+})
+```
diff --git a/neuraldb-docs/pages/sdk-javascript.md b/neuraldb-docs/pages/sdk-javascript.md
new file mode 100644
index 0000000..b698ebb
--- /dev/null
+++ b/neuraldb-docs/pages/sdk-javascript.md
@@ -0,0 +1,80 @@
+---
+title: JavaScript SDK
+sort: 110
+section-id: client-sdks
+keywords: JavaScript, TypeScript, SDK, Node.js, browser, npm, client
+description: The NeuralDB JavaScript/TypeScript SDK for Node.js and browser environments
+language: en
+---
+
+# JavaScript SDK
+
+## Installation
+
+```bash
+npm install @neuraldb/client
+```
+
+## Basic Setup
+
+```typescript
+import { NeuralDB } from '@neuraldb/client';
+
+const client = new NeuralDB({
+  connectionString: process.env.NEURALDB_URL!,
+  ssl: { rejectUnauthorized: true },
+});
+await client.connect();
+```
+
+### Connection Pool
+
+```typescript
+import { NeuralDBPool } from '@neuraldb/client';
+
+const pool = new NeuralDBPool({
+  connectionString: process.env.NEURALDB_URL!,
+  max: 20,
+  idleTimeoutMillis: 30000,
+});
+```
+
+## Vector Operations
+
+```typescript
+import { toVector } from '@neuraldb/client';
+
+await client.query(
+  'INSERT INTO documents (content, embedding) VALUES ($1, $2)',
+  ['My document content', toVector([0.023, -0.187, 0.412])]
+);
+
+async function semanticSearch(query: string, limit = 10) {
+  const embeddingResponse = await openai.embeddings.create({
+    model: 'text-embedding-3-small', input: query,
+  });
+  const queryVector = embeddingResponse.data[0].embedding;
+  const { rows } = await client.query<{ id: string; content: string; similarity: number }>(
+    `SELECT id, content, 1 - (embedding <=> $1) AS similarity
+     FROM documents WHERE embedding IS NOT NULL
+     ORDER BY embedding <=> $1 LIMIT $2`,
+    [toVector(queryVector), limit]
+  );
+  return rows;
+}
+```
+
+## High-Level Document API
+
+```typescript
+import { DocumentStore } from '@neuraldb/client';
+
+const store = new DocumentStore(client, {
+  table: 'documents',
+  embeddingColumn: 'embedding',
+  embeddingModel: { provider: 'openai', model: 'text-embedding-3-small', apiKey: process.env.OPENAI_API_KEY! },
+});
+
+await store.add([{ content: 'First document', metadata: { source: 'web' } }]);
+const results = await store.search('query text', { limit: 10, filter: { source: 'web' } });
+```
diff --git a/neuraldb-docs/pages/sdk-python.md b/neuraldb-docs/pages/sdk-python.md
new file mode 100644
index 0000000..709c2f4
--- /dev/null
+++ b/neuraldb-docs/pages/sdk-python.md
@@ -0,0 +1,90 @@
+---
+title: Python SDK
+sort: 100
+section-id: client-sdks
+keywords: Python, SDK, client, connection, CRUD, vector operations, psycopg
+description: Installing and using the NeuralDB Python SDK — connection, CRUD, and vector operations
+language: en
+---
+
+# Python SDK
+
+Built on `psycopg3` with NeuralDB-specific helpers for vector operations and batch ingestion.
+
+## Installation
+
+```bash
+pip install neuraldb
+pip install neuraldb[asyncio]  # async support
+```
+
+## Connecting
+
+```python
+from neuraldb import NeuralDB
+
+client = NeuralDB("postgresql://neuraldb:password@localhost:5432/mydb")
+
+# Async
+from neuraldb import AsyncNeuralDB
+async with AsyncNeuralDB("postgresql://...") as client:
+    result = await client.query("SELECT 1")
+
+# Pool
+from neuraldb import NeuralDBPool
+pool = NeuralDBPool("postgresql://...", min_size=5, max_size=20)
+with pool.acquire() as client:
+    result = client.query("SELECT COUNT(*) FROM documents")
+```
+
+## CRUD Operations
+
+```python
+from neuraldb import Vector
+
+client.execute(
+    "INSERT INTO documents (content, source, embedding) VALUES (%s, %s, %s)",
+    ("My document content", "web-scraper", Vector([0.023, -0.187, 0.412]))
+)
+
+rows = client.query("SELECT id, content FROM documents WHERE source = %s", ("web-scraper",))
+for row in rows:
+    print(row["id"], row["content"])
+
+client.execute("UPDATE documents SET content = %s, embedding = %s WHERE id = %s",
+    ("Updated", Vector(new_embedding), doc_id))
+client.execute("DELETE FROM documents WHERE id = %s", (doc_id,))
+```
+
+## Vector Search
+
+```python
+results = client.query("""
+    SELECT id, content, 1 - (embedding <=> %s) AS similarity
+    FROM documents WHERE embedding IS NOT NULL
+    ORDER BY embedding <=> %s LIMIT 10
+""", (Vector(query_embedding), Vector(query_embedding)))
+```
+
+## Transactions
+
+```python
+with client.transaction():
+    client.execute("INSERT INTO documents (content, embedding) VALUES (%s, %s)", (content, Vector(embedding)))
+    client.execute("UPDATE stats SET count = count + 1")
+```
+
+## Bulk Ingestion
+
+```python
+from neuraldb import BulkIngestor
+
+ingestor = BulkIngestor(client, table="documents",
+    columns=["content", "source", "embedding"], batch_size=1000,
+    embedding_model="openai/text-embedding-3-small", embedding_column="embedding", text_column="content")
+
+with ingestor as ing:
+    for doc in docs:
+        ing.add(doc)
+print(f"Ingested {ingestor.total_inserted} documents")
+```
diff --git a/neuraldb-docs/pages/sdk-rest.md b/neuraldb-docs/pages/sdk-rest.md
new file mode 100644
index 0000000..38109e2
--- /dev/null
+++ b/neuraldb-docs/pages/sdk-rest.md
@@ -0,0 +1,85 @@
+---
+title: REST API
+sort: 130
+section-id: client-sdks
+keywords: REST API, HTTP, endpoints, authentication, JSON, API
+description: NeuralDB REST API reference — all endpoints, authentication headers, and response formats
+language: en
+---
+
+# REST API
+
+## Base URL
+
+```
+https://your-neuraldb-host:8080/api/v1
+```
+
+## Authentication
+
+```
+Authorization: Bearer ndb_live_your_api_key_here
+```
+
+## Query Endpoint
+
+```http
+POST /api/v1/query
+Content-Type: application/json
+Authorization: Bearer ndb_live_...
+
+{
+  "query": "SELECT id, content, 1 - (embedding <=> $1) AS similarity FROM documents ORDER BY embedding <=> $1 LIMIT 5",
+  "params": [[0.023, -0.187, 0.412]],
+  "database": "mydb"
+}
+```
+
+Response:
+
+```json
+{
+  "rows": [{"id": "uuid-1", "content": "First document", "similarity": 0.923}],
+  "rowCount": 1,
+  "executionTimeMs": 3.2
+}
+```
+
+## Document Endpoints
+
+### Insert
+
+```http
+POST /api/v1/collections/my_docs/documents
+
+{"documents": [{"content": "NeuralDB is an AI-native database", "metadata": {"source": "blog"}}],
+ "embedding_model": "openai/text-embedding-3-small"}
+```
+
+### Search
+
+```http
+POST /api/v1/collections/my_docs/search
+
+{"query": "AI-native database", "limit": 10, "min_similarity": 0.7,
+ "filters": {"category": "technology"}, "embedding_model": "openai/text-embedding-3-small"}
+```
+
+## Error Codes
+
+| HTTP Status | Error Code | Description |
+|-------------|-----------|-------------|
+| 400 | `QUERY_ERROR` | Invalid NQL query |
+| 401 | `UNAUTHORIZED` | Missing or invalid API key |
+| 403 | `FORBIDDEN` | Insufficient role permissions |
+| 404 | `NOT_FOUND` | Document or collection not found |
+| 429 | `RATE_LIMITED` | Too many requests |
+| 500 | `INTERNAL_ERROR` | Server error |
+
+## Rate Limits
+
+| Plan | Queries/min | Documents/min |
+|------|------------|---------------|
+| Starter | 30 | 100 |
+| Developer | 300 | 1,000 |
+| Business | 3,000 | 10,000 |