
How to give AI agents safe access to your business data
A practical guide for letting ChatGPT, Claude, MCP servers, and custom AI agents query your business data without leaking PII, blowing up your warehouse bill, or giving an LLM root access.

A practical guide for letting ChatGPT, Claude, MCP servers, and custom AI agents query your business data without leaking PII, blowing up your warehouse bill, or giving an LLM root access.

At Basedash, we built an AI agent that acts as a 24/7 data analyst and PM—analyzing all our business data, surfacing insights, and guiding product decisions. The result? A 10× increase in activation rate and faster growth than ever.

A workflow guide to building a board reporting dashboard. Covers the four sections to include, the metrics that belong in each, layout patterns, narrative, and cadence.

A practical four-layer framework for BI permissions: identity, workspace, dataset, and row level. Covers role design, common mistakes, and how major BI tools enforce access.
“We evaluated Omni and other BI tools, but the speed to insight with Basedash is unmatched.”
Greg Demoge
Co-founder & CPO · FullEnrich
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“Before Basedash, reports could take weeks of back and forth. Now, they can be ready in hours.”
Claudio Godoy
AI Agents Lead · Taxfyle
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BI demos all look the same. Use this 45-question checklist across data, modeling, AI features, governance, pricing, and support to evaluate vendors honestly.

Multi-tenant analytics architecture for SaaS embedded dashboards. Compares silo, pool, and bridge models, where to enforce tenant isolation, and common mistakes.

A practical guide to building a HubSpot analytics dashboard. Metrics, data model, common pitfalls, and tools that fit revenue ops teams.

A step-by-step Looker migration playbook: audit LookML, choose a replacement, rebuild dashboards, run cutover, and decommission. Includes a checklist.

Most SaaS teams track KPIs in a flat list. A metric tree connects a north-star metric to the drivers and inputs people can actually move. Here's how to build one.

A practical guide to building a funnel analysis dashboard from event data. Covers SQL patterns, time windows, segmentation, layout, and the mistakes that quietly break funnel charts.

AI data analyst tools look similar in demos but behave very differently in production. Use this 5-dimension framework to evaluate 8 leading platforms in 2026.

A practical playbook for cutting Snowflake, BigQuery, Redshift, and Databricks bills driven by BI dashboards. Diagnosis, SQL fixes, caching, Basedash Warehouse, and governance.

An intake and triage framework for data requests: a request taxonomy, a priority and effort matrix, an SLA model, and rules for what not to build.

A step-by-step guide to building a customer success dashboard for SaaS teams. Covers metrics, data sources, layout patterns, common mistakes, and rollout.

How modern BI teams version control dashboards, metrics, and semantic layers with Git. Compares Looker, dbt, Power BI projects, Cube, Hex, and AI-native tools, plus a maturity model.

A practical review workflow for AI-generated SQL: a ten-point checklist, the failure modes that show up most often, and a rubric for when to trust the query.
We can help you migrate your data and dashboards from any other tool.