What is built, and what is next.
Three lists, in the order things actually move. Shipped means it runs today, building means it is underway, exploring means the shape is not settled.
Shipped
Running in the product today, against real sources.
Read-only connections
PostgreSQL, MySQL, SQL Server, ClickHouse, Trino, DuckDB, CSV and Excel. Each one is tested under a timeout, introspected, and rolled back completely if anything fails.
A semantic model per source
Introspection turns tables into models and foreign keys into relationships, giving every data source a graph the compiler can reason over.
Structured query engine
Converts natural language questions into verified, structured queries optimized for your connected database.
Automatic double-count prevention
Prevents inflated numbers across connected records, with clear explanations if data is incomplete or ambiguous.
Calculation source transparency
Every result clearly indicates how it was calculated, displaying verification status directly on charts and dashboard tiles.
Chat, charts and dashboards
Persistent sessions bound to a source you choose, validated charts that carry their own rows, and pinned tiles that re-run their query rather than showing a stale picture.
The sandboxed interpreter
Python for the harder maths, in a per-session container with the result set as a file, capped resources, no network by default, and no credentials.
Workspaces, projects and roles
Owner, admin, member and viewer, resolved at the API boundary. Non-members receive a 404, and a workspace can never be left without an owner.
Building
Designed and underway. These are the next things to land.
Semantic authoring in the app
Curate the generated model without leaving iDash: rename fields into business language, hide what should not be queried, and mark which tables are safe to aggregate.
Curated named metrics
Define revenue once, with its filters and its grain, so every question that mentions it resolves to the same calculation on every surface.
Granular data access control
Enforce row and column permissions so users only see the specific records authorized for their role.
Multi-entity deduplication
Enhanced calculation handling for complex queries combining multiple interconnected data sources simultaneously.
Warehouse connectors
Snowflake, BigQuery, Redshift and Databricks on the same path as everything else: read-only credential, introspection pass, and the same dedup guarantee.
Resilient streaming responses
Maintains smooth real-time response streaming for complex, long-running queries without timeout interruptions.
Smart query recovery
Automatically distinguishes temporary connection issues from data errors, retrying recoverable requests with clear error feedback.
Per-source prompts and memory
Guidance attached to a data source, and corrections a team makes once. The vocabulary of your business should not have to be re-taught every session.
Exploring
Researched, wanted, and not yet settled in shape.
Reuse of verified answers
Recall a prior question and its query only after a human has verified that pair, gated on that verification rather than on similarity alone.
Flexible unique record detection
Expand support for identifying unique records automatically even without explicitly defined primary keys.
Curation that survives a migration
Reconnecting regenerates the model wholesale today. Human curation should survive a schema change, with a clear diff of what moved.
Files as a first class source
A proper upload flow for spreadsheets and extracts, which is also what brings the working SQLite connector back into the catalog.
Standard integration endpoints
Allow external business tools and AI assistants to query your verified metrics using the same reliable definitions.
Start with what already ships.
The shipped list is enough to answer a real question about your own data today.