FAQ

An AI-native business intelligence (BI) platform uses natural language processing to let teams query databases, generate dashboards, and explore analytics without writing SQL. Basedash lets teams ask questions in plain English and turn answers into dashboards, automations, and shared workflows quickly. It covers the full data stack in one product — storage, data syncing, a semantic layer for trusted metrics, and the BI and reporting on top — so analytics stay consistent from raw data to final dashboard. The result is faster time-to-insight with less manual query work and fewer handoffs between teams.

AI-native BI is significantly faster and less brittle than traditional BI for day-to-day analytics work. Traditional tools often depend on SQL-heavy workflows, manual dashboard setup, and multiple rounds of query iteration before teams get a usable answer. Basedash reduces that friction by moving from plain-English prompt to governed chart quickly, so teams ship dashboards in minutes instead of waiting on longer setup cycles.

General AI tools are useful for brainstorming, but Basedash is purpose-built for production analytics. Basedash connects directly to your real data stack, understands your database schema, tables, and metric definitions, and returns answers grounded in governed sources. It is designed for teams with shared dashboards, reusable metrics, role-based access controls, and deployment options that match security and compliance requirements. The result is faster time-to-insight with more consistent and trustworthy answers.

Basedash is designed to prioritize accuracy in production analytics. Instead of generating freeform answers, it translates natural language into structured queries that are validated and executed directly against your connected databases or warehouses. Responses are grounded in your actual schema, tables, and governed metric definitions. Generated queries can be reviewed, traced to underlying data, and re-run for verification. By constraining AI within your real data environment and enforcing shared definitions and access controls, Basedash combines AI speed with the reliability of traditional BI systems.

No. Teams can ask business questions in plain English and generate dashboards without writing SQL for every request. Technical users still have the controls they need to validate logic, define trusted metrics, and govern shared reporting standards. This enables broader self-serve analytics without losing analytical rigor.

Basedash is built for cross-functional teams that depend on trusted analytics to make decisions quickly. Product, growth, marketing, sales, finance, and operations teams can use shared dashboards and governed metrics in one workspace. It works for both technical and non-technical users, so analysis does not bottleneck on a small analytics team.

Basedash supports 750+ integrations across databases, warehouses, and SaaS tools, including PostgreSQL, MySQL, Snowflake, BigQuery, Salesforce, HubSpot, Stripe, and Google Analytics. Teams can connect their existing stack directly and keep analysis grounded in governed business data. You can explore the full integration catalog and source categories on the data sources page.

Yes. Teams can connect existing warehouses directly and keep analytics in sync with current data architecture. For teams that want a faster path to unified analytics, Basedash Warehouse is also available. This gives organizations flexibility without forcing a major stack migration.

Yes. Basedash queries connected databases and warehouses directly, so answers and dashboards reflect current data at query time rather than a stale extract. Dashboards refresh on a live cadence, scheduled automations deliver up-to-date reports to Slack and email, and SaaS sources synced through the Basedash Warehouse update on their sync schedule. Teams that need current numbers for operations, revenue, or incident response do not have to wait for a nightly batch.

Yes. Basedash Insights runs daily AI analysis across all connected data sources and flags anomalies, trend breaks, and milestones with a written explanation of what changed and why it matters. Alerts can be sent to Slack or email, and Automations can run custom analysis on a schedule or when data changes. This gives teams proactive monitoring without building manual alerting rules for every metric.

Yes. Basedash exposes a remote Model Context Protocol (MCP) server, so AI clients like Claude Code, Cursor, ChatGPT, and Windsurf can ask governed analytics questions against your live company data. MCP calls authenticate with browser-based OAuth and enforce the same workspace permissions, source-level access controls, and semantic definitions as the Basedash app.

Yes. Basedash supports embedded analytics for teams that want to deliver dashboards and insights directly inside their product experience. This helps customer-facing teams share analytics in context instead of sending users to a separate BI tool. You can learn more on the embedding page.

Basedash is designed for production and enterprise environments with concrete security controls, including SOC 2 Type II compliance, encryption in transit and at rest, and strict data access boundaries. Customer data is never used to train models. Teams can enforce role-based access controls, single sign-on (SSO), SCIM user provisioning, and native audit logs, define trusted metrics, and deploy in self-hosted or VPC-based environments to meet internal network and compliance requirements. This allows organizations to adopt AI-native analytics without compromising their existing security posture.

No. Customer data is not used to train models. Basedash is built with strict data boundaries so teams can use AI-native analytics without exposing sensitive business data for model training. This is especially important for production, enterprise, and regulated environments.

Yes. Basedash includes a built-in semantic layer, where teams save trusted metrics as reusable SQL definitions and reference them across charts, dashboards, chat, insights, and automations. Because the AI reuses the same definitions, shared metrics stay consistent, conflicting numbers go away, and everyone works from one source of truth. This is a key advantage when scaling analytics across departments.

Yes. Every team can start with a 14-day free trial with no credit card required and full feature access. You can connect your data, generate dashboards, and validate how AI-native analytics fits your reporting process before committing. This lets teams assess value quickly without a long procurement cycle upfront.

The Startup plan is $1,000/month plus AI usage and includes up to 25 users, the Basedash Warehouse, 750+ data sources, automations, insights, the Slack app, and the MCP server. Enterprise plans are custom and add SSO, SCIM, audit logs, embedding, custom AI models, and self-hosting. Basedash uses flat-rate team pricing rather than per-seat licensing, so adding viewers and stakeholders does not create a new license cost. See the pricing page for full plan details.

Yes. Basedash supports enterprise procurement workflows, including security reviews, legal review cycles, and stakeholder sign-off processes. The team also helps with rollout planning so implementation aligns with internal approval requirements. This reduces friction from evaluation to production launch.

Yes. Teams commonly migrate from legacy BI tools and internal dashboards to simplify workflows and reduce dashboard-building friction. Basedash supports phased rollouts, so you can prioritize high-impact metrics and use cases first. This approach lowers migration risk while delivering faster wins for stakeholders.

The fastest way to start is to create a trial workspace, connect your core data sources, and define your first decision-critical metrics. You can sign up immediately, then expand to team-wide dashboards and reporting once initial workflows are validated. If you want help with setup, migration, or rollout planning, book a call with the team.