Vantage

An executive doesn't need twelve tabs open to understand the state of the business. They need to know what changed, why, and what they should do next.

The Smart Cockpit is that personalized intelligence layer: real-time KPIs with trends, AI-generated insights categorized by domain (Sales, Customer Success, Operations…), actionable recommendations, and relevant industry news. It's not another dashboard. It's the entry point that synthesizes everything else on the platform.

The gap between "I have a question" and "I need to file a ticket with the data team" slows every organization down. Chat bridges that gap by letting users ask questions in plain language and get answers grounded in governed datasets.

The assistant generates SQL, runs the query, explains results, and recommends follow-up analyses. Answers display as tables or charts. And because every response starts from a documented dataset with its schema and quality rules, you always know where the number comes from.

Business teams want to explore their data without waiting for an analyst to build every visualization. But classic self-service often creates ungoverned, duplicated reports that are impossible to maintain.

The Builder resolves this dilemma: create figures (charts or tables) from datasets you're authorized to access. Drag and drop dimensions and metrics, apply filters, choose visualization types, all within a framework where every figure stays linked to a governed, traceable dataset, shareable according to your team's permissions.

In most organizations, trust is attached to dashboards. But a dashboard is just a view: it can be outdated, miscalculated, or based on unverified data. Vantage inverts this logic: trust lives at the dataset level.

Customers, orders, revenue, campaigns: each dataset is a first-class asset with documented schema, ownership, validation rules, health score, lineage, and access controls. It's the foundation every KPI, figure, dashboard, and AI answer builds on.

"Emails shouldn't be empty": everyone knows it, but nobody checks systematically. Until the day a dashboard shows an aberrant conversion rate because 40% of emails are null.

The validation module, inspired by Great Expectations, lets you define business rules once and execute them automatically on every dataset update. Email format, positive values, approved status lists: assumptions become enforceable contracts, with a state history for each rule.

A dataset can be technically "valid" while silently degrading: volume dropping, columns emptying out, distributions drifting. The Quality module continuously monitors each dataset and assigns a health score from 0 to 100.

This score aggregates measurable signals (freshness, completion rate, volume stability, anomalies, validation results), with evolution over time to catch regressions. Early-warning alerts surface weak signals (excessive nulls, suspicious cardinality, volume changes) before they impact decisions.

A field renamed in Salesforce, a table dropped in the warehouse, a pipeline failing silently: without operational lineage, the impact propagates to dashboards before anyone understands the root cause.

Vantage maps the full journey: data source → pipeline → dataset → KPIs, figures, dashboards, and AI assistant. This isn't static documentation drawn once. It's a living view that updates with the platform, so data and business teams share the same understanding of dependencies.

How many times have two teams used the same KPI name with different calculations? The KPI catalogue centralizes definitions, links them to governed datasets, and exposes values over configurable time periods.

Each KPI is an asset in its own right: documented definition, source dataset, display configuration, and access permissions. No more debates about "which number is right": the answer is in the catalogue.

Organizations have already invested in Tableau, Power BI, or internal tools. Vantage doesn't try to replace them. It integrates them, links them to governed datasets, and adds a trust layer.

Register a dashboard (built in the dedicated lightweight app or embedded via iframe), associate it with source datasets, and expose the trust score, last refresh date, and governance metadata. An AI summary can even synthesize the content for time-pressed users.

Data governance often fails because permissions are either too broad (everyone sees everything) or too rigid (nobody can do anything). Vantage structures access in three complementary layers.

At the platform level, each user is Admin or User. At the organization level, the role defines which features are accessible (Chat, Builder, KPI catalogue…). At the team level, permissions apply to specific resources: this dashboard, this dataset, this KPI. Result: a marketing analyst accesses Chat and Builder, but only sees their team's datasets and dashboards.

Vantage is designed from the ground up as a multi-tenant platform. The same core engine powers different organizations with their own branding, data models, pipelines, and workflows, without rebuilding the platform for each deployment.

Dauntless is the illustration: same architecture, fully customized brand and experience. Modular activation lets you deploy only the capabilities relevant to each client.