Internal search with context — trained on your data.
MergeBase transforms your organization's tables, documents, and connected data into a searchable, structured knowledge layer. This content becomes the foundation for everything your system touches: automations, dashboards, AI agents, and internal tools.
It's not just storage. It's what your system refers to when it needs to reason, respond, or route, just like a person would reference a manual, guide, or historical record before making a decision.
In MergeBase, knowledge lives in tables — native tables you create and populate directly, or tables synced in from an external source. This includes:
Once a table exists, its content can be searched, queried by AI agents, or referenced directly inside any workflow.
Knowledge in MergeBase isn't isolated. It becomes active infrastructure used throughout your system:
AI Agents Agents reference table content when generating responses, answering questions, or summarizing content.
Workflows Workflow nodes (Start, Add Record, and others) can read from and write to MergeBase tables directly — for lookups, validation, routing, or logging.
Column Enrichment The AI Column feature lets you describe a new column in plain language (e.g. "sum totals by GL Code") and get a proposed formula with a live preview before it's added to the table.
MergeBase supports two complementary ways of finding relevant data:
Semantic Search Finds content based on meaning, not just keywords, using AI embeddings. This helps agents retrieve accurate answers even when terms differ from what's in the table.
To improve retrieval:
Structured Filtering Fields marked with the Filter role can be used to narrow results by exact values — status, category, date range, or connection — alongside a semantic query. This is how you scope retrieval to "only active records" or "only this connected source," rather than a separate named search mode.
Step 1: Create or Connect a Table
Step 2: Configure AI Search For any table, open Configure AI Search to assign each field a role — Vectorize, Filter, Display, or Ignore — so the system knows what to embed for search versus what to use for structured filtering.
Step 3: Start Using It Across the System Once configured, table data can be:
Centralized logic Store rules, references, and business data in one maintainable place
System-wide usage Power AI, workflows, and dashboards with the same trusted tables
Query-ready Configure a table once, then reference it consistently across every workflow and agent
Operational clarity and consistency Everyone — and everything — works from the same source of truth
