Search indexes and vector stores are only as good as their freshness. Aestus turns every change in your systems of record into updated search entries and embeddings: new text is embedded, deleted records disappear from results, and permission changes arrive quickly.
How Aestus reads changes from each system, and how it writes them.
As a source: Scans, for migrations between clusters.
As a target: Search documents with text, metadata and vectors, guarded by the engine's own external versioning.
As a source: Scans, for migrations between vector stores.
As a target: Vectors with metadata; all chunks of a record replaced together; deletes remove every chunk.
As a source: Through the host database's change capture.
As a target: pgvector in PostgreSQL, Atlas Vector Search in MongoDB, and vector columns in Oracle 23ai, SQL Server 2025 and Cassandra 5, with every guarantee of those targets.
As a source: Through the platform.
As a target: Aestus keeps the source tables fresh; the platform computes the embeddings and syncs its index from them.
Between systems of the same kind, and across kinds through a transform.
Chosen text fields are split into chunks and embedded with the model you choose, in your own network or at your provider.
Documents become search entries with their metadata, for full-text, filtered and hybrid search.
Store embeddings as columns in PostgreSQL, MongoDB, Oracle or SQL Server, inside the same transactional guarantees.
Move indexes between vector stores or search clusters, with a resumable copy and a reconciliation report.
Keep a copy of your embeddings in Delta tables, so building a new index does not mean paying for the embeddings again.
Feed Databricks or Snowflake, and let their own vector search keep the index in step.
Support and internal assistants answer from current tickets, manuals and policies, not last month's export (RAG).
Search by meaning, with prices, stock and availability that are always current.
Keep an Elasticsearch or OpenSearch index in step with the database behind your site or application.
When access to a record is revoked, it disappears from search results too, quickly.
Find similar customers, products or documents as they are created.
Move between vector stores, or run two side by side, without rebuilding your pipeline.
When a text gets shorter, its old chunks are removed; a deleted record removes all of them.
A change that does not touch the embedded text updates the metadata only, with no model call.
Embeddings are computed inside your network, with the provider you choose or a model you host.
Changing the embedding model rebuilds a fresh index alongside the old one, then switches over.
Operational, reporting and analytical SQL databases, from PostgreSQL to the lakehouse.
ExploreDocument, key-value and wide-column stores: MongoDB and its relatives, DynamoDB, Firestore and Cassandra.
ExploreProperty graphs in Neo4j and Neptune, and graphs kept inside PostgreSQL, MongoDB, SQL Server and Oracle.
ExploreApplication APIs and the enterprise message brokers: deliver changes into systems and onto event streams, or start a flow from them.
ExploreWant to discuss your use case? Write to [email protected].