What You Can Use It For
Collections shines whenever your data fits in a table and you want AI on top of it:Sales Pipeline Tracking
Import your deals from a CSV export. Ask an agent “What’s the total value of deals in negotiation?” and get an exact answer — computed, not guessed.
Customer Feedback Analysis
Centralize feedback, NPS scores, and survey answers. Let agents summarize sentiment trends and surface the most critical verbatims.
Product Catalogs
Keep a live catalog of products, prices, and stock. A support agent can answer “Is SKU X in stock?” from the source of truth.
Operational Logs
Store error logs or incident records. An agent aggregates them by severity and service to spot what needs attention.
Persistent Agent Memory
Give agents a durable place to record what they learn — leads captured in conversation, tasks completed, form answers — structured and queryable later.
The Collections Workspace
The product is organized around four areas, accessible from the sidebar:- Dashboard — key metrics at a glance: number of collections, total rows, queries and activity over the last 7 days, plus your most recent collections
- Collections — the full list of your collections, with search, creation, and deletion
- Visualizations — saved charts and dashboards built from your collections
- Settings — the MCP endpoint your agents use, and usage statistics
How It Works
1
Create a collection
Start empty or upload a CSV file. Column names and types (text, number, date, boolean, URL) are detected automatically from your data.
2
Review and manage your data
Browse records in a paginated table. Sort by any column, select rows, and delete what you no longer need.
3
Connect your agents
Link a collection to an agent in Agent Creator. The agent gains tools to query, count, aggregate, and update the data during conversations.
4
Visualize
Build charts (bar, line, pie) on top of your collections to track distributions and trends.
Key Concepts
Collections and Records
A collection is a named dataset with a schema — a set of typed columns. Each row is a record. Schemas are flexible: agents can add new fields over time without migrations, and the columns adapt.Typed Columns
Every column has a type —text, number, date, boolean, or url — inferred automatically when you import data. Types drive how values are displayed (formatted numbers, Yes/No booleans, clickable links) and how they can be filtered and aggregated.
Exact Queries, Not Retrieval
Agents query collections with structured filters and aggregations (count, sum, average, group by). Results are exact and deterministic — ideal for questions where an approximate answer is not acceptable.Ownership and Isolation
Each collection belongs to its creator, and agent data is isolated by default: an agent only sees the collections it has been granted access to. You can share a collection with several agents when they need to work on the same data.Collections vs. Knowledges
Available soon — a capability store will let you extend your collections with hooks (pre- and post-processing of your data) and connectors (Salesforce, HubSpot, and more).
Getting Started
1
Open Collections
From the Prisme.ai home, open the Collections product.
2
Create your first collection
Click New Collection and upload a CSV export (up to 50 MB) — a sales report, a contact list, anything tabular.
3
Check the detected schema
Review the column types inferred from your file and the data preview, then confirm.
4
Link it to an agent
In Agent Creator, give an agent access to the collection and start asking questions about your data.
Next Steps
Managing Collections
Create, organize, and delete collections
Importing Data
Upload CSV files with automatic schema detection
Working with Data
Browse, sort, and manage your records
Using Collections with Agents
Give your agents read and write access to your data