Search & Discovery

Servantium provides two powerful search systems that work together: Full-Text Search for finding specific records by name or keyword, and Semantic Vector Search for discovering related concepts and past work.

Full-Text Search (Algolia)

Servantium uses Algolia to power fast, typo-tolerant, full-text search across your organization’s data.

Keyboard Shortcuts

You can instantly focus the table search bar on any list screen by pressing Cmd+F (Mac) or Ctrl+F (Windows).

How it works

1. Automatic Indexing — A backend trigger (sync_to_algolia) listens for changes across your core collections (Engagements, Quotes, Accounts, Contacts, Items).
2. Instant Updates — Whenever a document is created, updated, or deleted, the change is immediately pushed to the search index.
3. Global Search — The global search bar in the top navigation queries this index across all collections simultaneously. Results appear in a floating overlay, allowing you to instantly jump to the specific URL of any record.

Security & Access Control

Search results automatically respect your organization’s role and tag-based access rules (ABAC). The system generates dynamic search filters based on the active user’s assigned roles and security tags. If a user does not have permission to view a specific collection or a record tagged with a restricted category, those records will be omitted from their search results.

Reindexing

In rare cases (such as after a major bulk data import), the search index might fall out of sync. Organization administrators can use the Management CLI to run the reindex_algolia.py script to perform a full synchronization from Firestore to Algolia.

Semantic Vector Search

While full-text search is great for finding a specific client by name, Semantic Search understands the meaning of your data.

How it works

Servantium uses Google Vertex AI (Gemini Embeddings) and Firestore Vector Search.

1. Note Consolidation — As team members add notes to an engagement, the noteEmbedding backend trigger aggregates all the text.
2. Embedding Generation — The text is passed to the Gemini Embedding model, which converts the concepts into a high-dimensional vector array.
3. Similarity Matching — When you use the “Similars” feature, the getsimilar Cloud Function performs a nearest-neighbor vector search. It compares your current engagement’s embedding against all historical engagements in your organization to find the closest conceptual matches.

Using Search in Object Fields

When designing Engagement Templates or Data Dictionaries, you can use the Object field type. This field uses Algolia’s search index to provide a dynamic dropdown that searches across a specific collection (e.g., Accounts or Contacts), rather than a hardcoded list of options.

What’s next?

• AI & Intelligence — Learn more about how semantic search powers AI quote generation.
• Engagement Templates — Configure templates that utilize Object search fields.