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.