Overview of Intelligence Navigator
8 min
Before data can drive a decision, it has to be turned into something people can understand and trust. Data analysis is the process of identifying, cleaning, transforming, and modeling data to discover meaningful information. Intelligence Navigator automates most of that process and pairs it with AI that explains what it finds in plain language.
Here is what each capability in the workspace does, and when you would reach for it.
Analytics and business intelligence
Analytics answers four kinds of questions: what happened (descriptive), why it happened (diagnostic), what will happen (predictive), and what you should do about it (prescriptive). Intelligence Navigator covers the full spectrum with KPI tracking, trend and revenue analysis, forecasting, sentiment analysis, dynamic pricing, and competitor intelligence.
Agentic AI
AI agents answer questions about your data in plain English. Data IQ chats with your structured data — spreadsheets, databases, CRM exports — while Knowledge IQ grounds answers in your documents and knowledge base. You can also build chatbots on top of them and put grounded assistants in front of your team or customers.
Vision AI
Vision AI reads documents the way a person would — PDFs, scans, invoices, and forms — and extracts structured data with confidence scores, so nothing has to be retyped by hand.
Automation
Automation lets the platform watch your data and act. Autopilot runs analyses on a schedule, Sentinel monitors metrics and alerts you when something moves off track, and workflows chain steps together so routine work happens without you.
Dashboards and reports
The dashboard builder turns your data into interactive dashboards without a BI team, and reports package insights for stakeholders — on demand or on a schedule.
Presentation AI
Presentation AI turns analysis into board-ready presentations, so the story of your data travels beyond the workspace.
OKRs
Objectives and key results connect insight to execution: set goals, cascade them across teams, check in weekly, and publish scorecards so everyone knows what matters and how it is trending.
Example
Consider a retail business. Analytics shows which products sold and forecasts next season's demand; sentiment analysis explains how customers feel about them; Vision AI captures supplier invoices; Sentinel raises a flag when margins slip; a dashboard keeps the whole picture in one place; and OKRs keep the team focused on the growth targets those insights support.
The underlying thread is trust: the platform captures data from your sources and shapes it into something consumable, meaningful, and easy to understand — so decisions can be made with confidence.