Basedash Suggestions is an AI BI feature set that proposes data questions, dashboard ideas, and automations based on your workspace context. It helps teams start analysis faster and turn recurring insights into scheduled workflows.
Basedash Suggestions is a feature set inside Basedash’s AI-native BI platform that surfaces next-step ideas across chat, dashboards, and automations. Instead of starting from an empty prompt or a fixed template library, users are shown data-specific questions, dashboard ideas, and recurring workflow suggestions generated from the workspace’s own context.
According to the product pages, those suggestions are shaped by connected tables, prior chats, existing dashboards and automations, user role, and custom AI context. Clicking a suggestion starts the analysis, builds the dashboard, or configures the automation, which makes the product useful for teams that want an analyst-style starting point for exploring data and sharing results.
On the chat home, the product surfaces data-specific questions instead of a blank prompt. Selecting one sends the question to the AI analyst, which explores the schema, writes and verifies queries, and returns an answer with a chart.
The dashboards area replaces generic templates with dashboard ideas generated from the workspace’s actual data. Each suggestion includes a name, a description, and a build plan, and the dashboard agent can construct the charts, layout, and underlying queries.
Automations are suggested from the same workspace context, but framed as recurring reports or alerting workflows. A suggestion can be turned into a configured automation with its trigger, schedule, and delivery target already set up.
Automations can run on a schedule, when data changes, or on demand. The feature supports fixed cadences such as daily, weekly, monthly, or quarterly runs, as well as data-change triggers and manual runs.
Results can be delivered directly to Slack or email, and in-app history keeps previous runs searchable. The pricing page also references a Slack app, which aligns with team delivery workflows.
Suggestions are personalized from connected data sources, prior chats, existing dashboards and automations, and workspace context. They regenerate as the workspace evolves, so dismissed or used suggestions are replaced with new ones.
A data analyst or operator can open chat and use a personalized suggestion to start a question about trends, anomalies, or cohort behavior without deciding the query from scratch.
A team that needs a reporting view can pick a dashboard suggestion based on its actual data, then let the dashboard agent assemble charts, layout, and the underlying queries.
Operations, finance, or product teams can turn recurring reporting needs into automations that run on a cadence or when data changes, then send results to Slack or email.
Founders and team leads can use the suggestion system to surface recurring questions or reports that reflect recent activity in the workspace, helping them spot issues they were not explicitly searching for.
Teams can use the suggested workflows as a starting point when they do not know which report, alert, or question to build next, replacing a template-first setup with a context-driven one.
Basedash Suggestions is rolling out to all Basedash workspaces and works on every plan. To get started, sign up or log in, connect a data source, then open the chat home where the first suggestions appear under the input.
Suggestions are generated from the tables in your connected sources, the chats you and your team have had, the dashboards and automations already in the workspace, and your role plus custom AI context.
The blog post says suggestions appear in chat, dashboards, and automations. On chat, they surface as questions; on dashboards, they appear as build suggestions with a name, description, and build plan; on automations, they appear as recurring reports or workflows the system can configure.
In the automations feature, Basedash can analyze connected data on a schedule, when data changes, or on demand, and deliver results to Slack or email. The pricing page also notes a Slack app and automation support on the Startup plan.
The source material does not describe a separate self-serve free tier beyond a 14-day free trial, and it does not provide a full public breakdown of all supported integrations on these pages. It does indicate support for connected data sources, Slack delivery, and a wide range of data connections.
Struere is an AI-native platform for turning spreadsheet data into structured operational software with dashboards, alerts, and automations. It is aimed at teams that want to replace manual spreadsheet workflows without building custom tools from scratch.
PromptScout tracks how ChatGPT, Gemini, Google AI Overviews, and Perplexity mention your brand or competitors, then pairs those results with source analysis and website audits. It helps teams decide what to fix in content, positioning, or site readiness next.
SaveMRR is a Stripe retention tool for SaaS teams that scans billing data for churn and MRR leaks, then automates recovery through dunning, cancel-save offers, win-back emails, and onboarding nudges. It is built for founders and bootstrapped teams using Stripe.
Hype is a web tool for finding trending YouTube topics by category, time range, and scoring mode. It helps creators spot emerging ideas, inspect source videos, and decide what to cover next.
Sleek Analytics is a privacy-friendly web analytics tool with real-time visitor tracking, Core Web Vitals, and revenue attribution. It helps site owners understand traffic and conversions without cookie banners or a heavy setup.
Codex Plugins bundle reusable skills, app integrations, and MCP servers into workflows you can install in the Codex app or use from Codex CLI. They help extend Codex with connected-service tasks, reusable instructions, and shared team workflows.
