Real-time
Two-way sync
Changes in Autopilot or AWS Aurora PostgreSQL instantly reflect in both systems. No stale data, no manual imports.
AI ⇄ Database
Autopilot to AWS Aurora PostgreSQL integration — real-time, two-way sync
Keep Autopilot and AWS Aurora PostgreSQL in sync without custom scripts. Cut weeks of integration work, eliminate silent data drift, and give your team a single, reliable source of truth.
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Adopted by fast-scaling companies moving mission-critical data in real time
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Why teams connect Autopilot and AWS Aurora PostgreSQL
Sync the records in AWS Aurora PostgreSQL into Autopilot and land its embeddings, classifications, and generated fields back on the same rows, in real time and without a pipeline to maintain.
AI systems do not hold customers or invoices the way business apps do. What they hold is derived from your data: the vectors and metadata in a vector store, or the classifications, extracted fields, and generated text a model produces over records it was given. AWS Aurora PostgreSQL is where those source records actually live. The bridge between the two is the row itself, since an item in Autopilot and the record in AWS Aurora PostgreSQL it describes are two halves of the same thing, and they drift the moment one is updated without the other.
Stacksync syncs Foreign keys, Replication slots and publications, Databases and schemas, Tables in AWS Aurora PostgreSQL with Journeys (Triggers), Activities, Contacts, Lists in Autopilot in real time. Rows created or changed in AWS Aurora PostgreSQL flow into Autopilot so inference and embedding run on current data, and the scores, labels, and generated fields Autopilot produces flow back onto the matching rows in AWS Aurora PostgreSQL, mapped field by field. A change on either side appears on the other within seconds, with no extraction job or webhook plumbing to keep alive.
Because matching is by a stable identifier, every row in AWS Aurora PostgreSQL stays tied to its AI-side counterpart in Autopilot. Retrieval, enrichment, and generated content always resolve back to the record they came from, so there are no orphaned vectors and no labels describing a version of a row that no longer exists.
Common use cases
- 01 Stream Activities into a warehouse so opens, clicks, and journey steps sit next to product and revenue data for lifecycle reporting.
- 02 Upsert Contacts from a CRM or product database into Autopilot so new signups and profile changes flow into email and SMS journeys without manual CSV imports.
- 03 Capture row-level changes with logical replication and propagate them to SaaS tools without batch jobs.
- 04 Sync JSONB-heavy application data into structured objects in downstream business systems.
Common sync patterns
Run the AI on current data
Rows created or changed in AWS Aurora PostgreSQL flow into Autopilot as they happen, so embeddings, classifications, and prompts run on the latest records instead of a nightly snapshot.
Write results back onto the record
Scores, labels, extracted fields, or generated text produced in Autopilot land on the matching row in AWS Aurora PostgreSQL, next to the source data your applications already query.
Keep derived data fresh as sources change
When a row in AWS Aurora PostgreSQL is updated or removed, its counterpart in Autopilot is updated or removed too, so nothing in Autopilot describes a record that has since changed or gone.
What you can sync between Autopilot and AWS Aurora PostgreSQL
Representative objects on each side — any object or custom field can map to any target. Schemas are auto-detected; types are converted between the two systems.
| Autopilot objects | AWS Aurora PostgreSQL objects | How this pairing syncs | |
|---|---|---|---|
| Smart Segments Rule-based dynamic audiences; membership is computed by Autopilot, so it is read-only over the API. | Primary keys and constraints Identify rows for upserts and enforce integrity on sync writes. | Smart Segments is specific to Autopilot and Primary keys and constraints to AWS Aurora PostgreSQL — each maps to any object or custom field on the other side. | |
| Journeys (Triggers) Automation journeys; a contact can be added to a journey via its trigger endpoint to start automated email or SMS sequences. | Views and materialized views Usable as read-only sources for filtered or precomputed sync datasets. | Journeys (Triggers) is specific to Autopilot and Views and materialized views to AWS Aurora PostgreSQL — each maps to any object or custom field on the other side. | |
| Activities Per-contact activity and event history (opens, clicks, journey steps); read-only feed used for engagement reporting. | Foreign keys Relationship metadata that syncs can translate into object references elsewhere. | Activities is specific to Autopilot and Foreign keys to AWS Aurora PostgreSQL — each maps to any object or custom field on the other side. | |
| Contacts Core people records (email, name, custom fields, list and segment membership); upserted two-way as the primary sync object. | Replication slots and publications The logical replication objects that power log-based CDC. | Contacts is specific to Autopilot and Replication slots and publications to AWS Aurora PostgreSQL — each maps to any object or custom field on the other side. | |
| Lists Static contact lists; membership is readable per list and writable by adding or removing contacts. | Databases and schemas PostgreSQL's two-level namespace scopes which tables a sync connection targets. | Lists is specific to Autopilot and Databases and schemas to AWS Aurora PostgreSQL — each maps to any object or custom field on the other side. | |
| Custom Fields User-defined contact properties (string, number, date, boolean); discovered so field keys map cleanly to destination columns. | Tables The core sync unit; rows are matched across systems by primary key. | Custom Fields is specific to Autopilot and Tables to AWS Aurora PostgreSQL — each maps to any object or custom field on the other side. |
How changes propagate between Autopilot and AWS Aurora PostgreSQL
Each direction of the sync is driven by what the source system can signal and what the destination accepts — detection, delivery, and expected latency below.
Autopilot AWS Aurora PostgreSQL Interval-based propagation
DetectionStacksync polls Autopilot for changes on an incremental schedule, reading only records changed since the previous pass. No CDC.
DeliveryEach detected change is applied to AWS Aurora PostgreSQL as a row-level write, with types converted between the two schemas.
AWS Aurora PostgreSQL Autopilot Sub-second propagation
DetectionChanges in AWS Aurora PostgreSQL are captured at the source via change data capture — no polling loop against its API. Log-based CDC via PostgreSQL logical replication (WAL decoding through replication slots), with timestamp polling as a fallback.
DeliveryEach detected change is written to Autopilot through its API, with automatic retries and rate-limit backoff.
Rate-limit considerations
- Autopilot: The REST API is capped at 100 requests/minute per account; exceeding it returns HTTP 429. Enterprise plans can request a higher limit. Contact writes accept an array for bulk upsert, and list and segment reads paginate via a bookmark cursor.
What ships with Autopilot ⇄ AWS Aurora PostgreSQL
Connect Autopilot and AWS Aurora PostgreSQL for flexible, real-time data sync.
Real-time sync, workflow automation, event queues, EDI, and monitoring, for every Autopilot–AWS Aurora PostgreSQL connection.
No-code + pro-code
Workflow automation
Trigger automated workflows whenever Autopilot or AWS Aurora PostgreSQL data changes, update records, fire webhooks, or kick off sequences without brittle API scripts.
At scale
Event queues
Handle millions of events per minute without losing a single Autopilot or AWS Aurora PostgreSQL record.
Observability
Monitoring
Track your Autopilot ⇄ AWS Aurora PostgreSQL sync health, view errors, and replay failed events in one click.
Trading partners
EDI
Transform legacy EDI complexity into simple database interactions between Autopilot and AWS Aurora PostgreSQL.
How the Autopilot and AWS Aurora PostgreSQL connectors work
Autopilot
- Integration surface
- REST API (Autopilot v1); Autopilot rebranded to Ortto in 2021 and the newer Ortto API co-exists with the legacy Autopilot endpoints
- Authentication
- Per-account API key sent in the autopilotapikey request header (generated in account settings); requests use Content-Type application/json against https://api2.autopilothq.com/v1/
- Change detection
- No CDC; incremental sync polls the /contacts endpoint with bookmark cursor pagination and updated timestamps. Journey webhook actions can push specific contact events, but there is no general change-subscription webhook, so polling is the reliable path.
- Capabilities
- read · write
- Rate limits
- The REST API is capped at 100 requests/minute per account; exceeding it returns HTTP 429. Enterprise plans can request a higher limit. Contact writes accept an array for bulk upsert, and list and segment reads paginate via a bookmark cursor.
AWS Aurora PostgreSQL
- Integration surface
- SQL wire protocol (PostgreSQL-compatible), standard Postgres drivers and JDBC
- Authentication
- Database credentials, optionally AWS IAM database authentication, over TLS
- Change detection
- Log-based CDC via PostgreSQL logical replication (WAL decoding through replication slots), with timestamp polling as a fallback
- Capabilities
- read · write · CDC
How it works
How to connect Autopilot to AWS Aurora PostgreSQL — three steps, no code
Configure and sync within minutes, no code. Whether you sync 50k or 100M+ records, Stacksync handles the queues, infra, and plumbing. Integrations are non-invasive and need zero setup on your systems.
- 01
Connect your apps
Authenticate Autopilot and AWS Aurora PostgreSQL with each platform's native method — OAuth, API keys, or service accounts — plus secure options like SSH tunneling, IP whitelisting, and VPC peering.
Autopilot
connected
AWS Aurora PostgreSQL
connected
OAuth 2.0SSH tunnelSSL certificateVPC peering - 02
Choose tables
Pick the Autopilot and AWS Aurora PostgreSQL objects to sync — Stacksync auto-detects both schemas, including custom fields where the platform exposes them. Sync to existing tables, or let Stacksync create new ones with ideal data types.
objects · Autopilot ⇄ AWS Aurora PostgreSQLCustomers 12,480Sales Orders 8,213Invoices 5,902Items 1,344 - 03
Map fields
Fields map automatically even when names and types differ. Stacksync handles transformation and type casting for you, zero configuration required.
Autopilot AWS Aurora PostgreSQLCompany company_name textEmail email textAmount amount numericCreated created_at timestamp
FAQ
Autopilot and AWS Aurora PostgreSQL integration FAQ
-
Yes. Stacksync provides a managed, real-time two-way integration between Autopilot and AWS Aurora PostgreSQL: authenticate both systems, choose the objects to sync (such as Autopilot's Smart Segments and Journeys (Triggers)), map fields visually, and changes propagate both ways in milliseconds — no code required.
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Stacksync pricing is usage-based and starts at $1,000/month, including the managed Autopilot and AWS Aurora PostgreSQL connectors, real-time two-way sync, monitoring, and support. That replaces building and maintaining a custom Autopilot–AWS Aurora PostgreSQL integration in-house.
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Yes — Stacksync ships production-grade connectors for both Autopilot and AWS Aurora PostgreSQL. The connectors handle authentication, schema detection, rate limits, and retries; you configure the sync, and Stacksync operates it.
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Change detection on Autopilot: No CDC; incremental sync polls the /contacts endpoint with bookmark cursor pagination and updated timestamps. Journey webhook actions can push specific contact events, but there is no general change-subscription webhook, so polling is the reliable path. On AWS Aurora PostgreSQL: Log-based CDC via PostgreSQL logical replication (WAL decoding through replication slots), with timestamp polling as a fallback. Each detected change propagates to the other side in milliseconds, with field-level conflict resolution and an inspectable event log.
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On the Autopilot side: Journeys (Triggers), Activities, Contacts, Lists, plus custom fields where Autopilot exposes them. On the AWS Aurora PostgreSQL side: Foreign keys, Replication slots and publications, Databases and schemas, Tables. Stacksync auto-detects both schemas and converts types between the two systems.
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Yes. Each object mapping can be bidirectional or restricted to a single direction (both systems accept writes). Read-only mirrors, one-way pushes, and full two-way sync can be mixed in the same integration.
SECURITY
Security teams trust Stacksync
As a data company, we understand the importance of keeping your data secure. Stacksync is built with security best practices to keep your data safe at every layer, and is DPF-certified for US, EU, UK and CH data transfers.
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ISO 27001
HIPAA BAA
GDPR
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→ SECURITY WITH BENEFITS
SSO & SCIM
Let your users access Stacksync from your centralized user management systems. Works with Okta, Azure, Google SSO and more.
Alerts
Immediately get alerted about record syncing issues over email, Slack, PagerDuty and WhatsApp. Resolve issues from a centralized dashboard with retry and revert options.
Secure connection options
Securely connects to your systems with:
Related integrations
Every pair below is a real-time, two-way sync. Search all 425 integrations available for Autopilot and AWS Aurora PostgreSQL.
Popular · 7 of 425
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Autopilot ⇄ Citus -
Autopilot ⇄ Couchbase -
Autopilot ⇄ DuckDB -
Autopilot ⇄ Elasticsearch -
AWS Aurora PostgreSQL ⇄ Azure OpenAI -
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AWS Aurora PostgreSQL ⇄ Openai
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