AutessaDB

Semantic search and AI

Vector search is a column setting, not a second database: flip it on and AutessaDB indexes, enriches and serves every value to people and agents.

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call_notes · Semantic Searchcolumn: summarySAMPLE

which calls mention pricing complaints?

#7712Customer said the new tier feels too expensive for a team of four0.88

#7690Asked whether annual billing lowers the per-seat cost0.81

#7655Comparing us with a cheaper tool before renewal0.74

Enriched on write: summary · key facts · likely questions. Filter alongside: status = 'open'.

-- why it matters

What does semantic search and ai cover?

AutessaDB has built-in vector search: mark a column for semantic search and every write is indexed, enriched by AI and ready for agents and RAG.

Semantic search is the reason a database for AI apps exists. In AutessaDB it is a column setting: no second database, no sync job. Text, documents and recordings become searchable by meaning, and agents get tools to use them.

Who works here

Builders mark columns for semantic search and create knowledge bases; agents search at run time; anyone can ask the DB Copilot to design or query.

In one sentence each

Semantic (vector) search · Mark a column for semantic search and every write is indexed in the background, enriched by AI, and searchable by meaning next to ordinary filters.
File and audio search · File columns get an auto-written notes companion and audio columns get a transcript, so documents and calls are searchable like text.
Database tools for AI agents · Ready-made tools give agents insert, update, delete, query, aggregate, semantic query, general search, Drive and data lake access to your tables.
DB Copilot · Describe the data you need and the DB Copilot proposes tables and schema changes as cards you apply or reject, and answers questions about your data.

-- subtopics

Everything in semantic search and ai

Semantic (vector) search

Mark a column for semantic search and every write is indexed in the background, enriched by AI, and searchable by meaning next to ordinary filters.

File and audio search

File columns get an auto-written notes companion and audio columns get a transcript, so documents and calls are searchable like text.

Database tools for AI agents

Ready-made tools give agents insert, update, delete, query, aggregate, semantic query, general search, Drive and data lake access to your tables.

DB Copilot

Describe the data you need and the DB Copilot proposes tables and schema changes as cards you apply or reject, and answers questions about your data.

-- on screen

What it looks like

File and audio search

contracts · file + notes companionvectorizedSAMPLE

filefile_notes (auto)call_audiotranscript (auto)

acme-msa.pdfMaster agreement, Net 45, 12.2 termination for convenience▶ 03:12Thanks for joining, about the renewal…

globex-sow.docxStatement of work, 3 milestones, fixed fee▶ 01:47Quick question on milestone two…

Trigger idea: transcript became non-null → invoke SummarizeCall tool.

Database tools for AI agents

SupportAgent · conversationruns as: dana@SAMPLE

How many open tickets mention refunds?

tool · AUTESSADB_SEMANTIC_QUERYtickets.body · “refund” · limit 10 · 7 matches

tool · AUTESSADB_AGGREGATECOUNT where status = open · 5

Five open tickets ask about refunds. The oldest is #2201, waiting 3 days.

-- faq

Questions about semantic search and ai

Do I need a separate vector database with AutessaDB?

No. Vector search is a column setting. The embedding index lives with the row, follows its security, and updates on every write.

Can AutessaDB search inside PDFs?

Yes. A vectorized File column gets an AI-written notes companion for each document, which is indexed for semantic search.

Do agents need a connector to use AutessaDB?

No. AutessaDB tool types take a table id and work directly.

Can the DB Copilot change my tables without asking?

No. Reads run directly, but every schema change appears as a card that you apply or reject, and the chat waits for your decision.

Other areas of AutessaDB

data_modelingTables and data modeling →AutessaDB tables are typed, related and computed: text, number, select and media columns, foreign keys, AutessaScript computed columns, write-once ledger tables and one-click knowledge bases.queries_and_apisQueries, APIs and SDKs →One query model everywhere: the same filters, sorts, aggregates and bulk writes work in the builder, the REST API, the CLI, scripts and agent tools.row_level_securityRow-level security and access →Every read and write, from a person, agent, script, app or API key, passes through the same row rules, column masks and access log.triggersTriggers and pipelines →Row changes become automation: triggers run tools, agents and writes, chain across tables, and leave a full execution history.data_lakeData lake and analytics →A columnar data lake for events, logs, history and exports: bulk loads, partitions, analytical SQL, a semantic layer, plain-language Ask, data agents and archive tiers, all under the same security as your tables.files_and_appsFiles, apps and operations →AutessaDB is also the backbone for files and apps: Drives, app sign-in tables and per-visitor rules, with soft delete as the safety net.

$ autessa autessadb tables create

Semantic search and AI, built into one database.

Use it out of the box, then shape it to how your company works. Tables, the data lake, security and triggers are all the same AutessaDB, from the builder, the API or the CLI.

Create a free database

Read the AutessaDB guide or install the CLI.

100% customizable, code and all

Make AutessaDB fully your own.

Use it out of the box, then change anything. Ask Autobot, Autessa’s AI builder, for a new field, screen, report, workflow or agent and it builds it into your copy of the app. Or open the code and edit it yourself. Nothing is locked: the app, its data and its code are yours.

Start free and make it yours