Strict rules in front.
Flexible intelligence behind.
AIDB is the AI-native data fabric that unifies structured, document and timeseries data into one governed store — queryable in natural language, atomic for compliance, fluent for AI.
Enterprises have spent a decade debating data warehouses versus data lakes — strict schemas versus AI-friendly sprawl. AIDB refuses the choice. Your ledger columns stay rigid, auditable and atomic. Your agent reasoning, context, and unstructured content live alongside them, embedded and searchable. Both commit in the same transaction. Both answer the same question.
What it is, in plain terms.
Structured collections
Schema-defined tables with full-text and hybrid vector search, read-only SQL for analysts, and proxy tables that route to existing databases you can’t move.
DocStore
PDFs, DOCX, Markdown — ingested, chunked, embedded, and searchable with ACLs per document. Your knowledge base becomes a first-class corpus.
Timeseries
Tagged, time-partitioned data with retention and rollups. One fabric for financial ticks, IoT, infrastructure and agent telemetry.
Natural-language querying
A registered LLM orchestrator routes questions to SQL, vector or hybrid search — and returns answers grounded in your governed data.
What changes for the business.
One place to govern, secure and query the data your AI needs — whether it’s a transaction row, a contract PDF or a sensor stream.
Financial columns and AI reasoning commit in the same transaction. No drift between what the ledger says and what the agent saw.
Business users ask in plain English. AIDB routes the query, enforces policy and returns grounded, traceable answers.
Enterprise-grade by default.
The "mullet architecture"
Business in front, AI in back. Structured columns carry contractual meaning; JSON/vector fields carry context, rationale and embeddings.
Hybrid search
Keyword plus semantic plus filters, fused into a single ranked result. Find the right invoice whether you remember its number or its intent.
Proxy tables for legacy systems
Route queries out to the databases you can’t yet retire. Agents still see a unified model; your SAP stack stays where it is.
Row-level ACLs
The same IAM policy that governs your app governs your data. Agents can only see what the asker could see.
Retention, rollups and lineage
Long-term retention, materialized aggregates and full lineage for regulated data — automated, not improvised.
Read-only SQL for analysts
Your BI team doesn’t have to learn new tools. They point their existing SQL clients at AIDB and get the same governance everyone else does.
AI is only as useful as the data it is grounded in.
The Inversion Principle assumes AI can be trusted to do the work. That trust collapses the moment an agent hallucinates a policy or invents a customer record.
AIDB is the grounding layer that makes AI-first safe. Every answer an agent gives is rooted in data that your business owns, governs and can reproduce. The ledger is still the ledger. The contract is still the contract. But now a question about either can be answered in natural language, in seconds, with evidence attached.
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