Big Data & AI

## Details

What if you could harness the power of AI for analytics, without sending a single byte of your data to the cloud?
In this 60-minute session, we’ll explore how professionals are now running full-featured, AI-powered analytics tools directly on their local machines. No external logins. No internet dependency. No approvals required.
Built for teams that work in secure, restricted, or compliance-sensitive environments, this session will show how you can keep queries, results, and credentials completely local, and still unlock fast, intelligent insights from your data.
What You’ll Learn

• How to set up a local AI analytics environment, with zero cloud exposure
• Query internal databases like Postgres, MySQL, or SQLite, even when offline
• Maintain total control over credentials, logs, and schema visibility
• Save and reuse logic for recurring questions across your team
• Build personal analytics workflows that are fast, persistent, and consistent

Who Should Attend

• Data and analytics teams working in secure or regulated environments
• Professionals who need local access to production-like databases
• Anyone looking for powerful, offline-friendly analytics without network risk

Boosting Team Productivity

• With desktop-based AI, teams no longer have to rely on browser sessions, scattered SQL scripts, or third-party dashboards. Instead:
• Reusable workflows ensure consistency across users
• Local installations mean faster access, zero latency, and no downtime
• Team members can collaborate on logic and results without reworking queries
• Everyone stays aligned and insights are easier to trace, reproduce, and share

Whether it’s a recurring metric, a last-minute audit, or a high-stakes decision, the answers are always at your fingertips.
No browser tabs. No round trips. No cloud uploads.
Just fast, reliable analytics, where your data already lives.
Reserve your seat now and see firsthand how AI can solve today’s most pressing analytics problems.

Every business has the same problem: the answer to their hardest question is never in one place.
Your database holds what actually happened - transactions, enrollments, conversions. Your analytics platform holds who showed up and what they did. Your ad accounts hold what you spent to get them there. Three systems. Three partial truths. And no single tool that reads all three at once.
Terno does.
In this session, we'll walk through two real cases where Terno reasoned across a production database, web analytics, and ad spend data to surface a diagnosis that no single system could have found on its own.
What we'll cover:

• How Terno connects to your database, analytics, and ad platforms and builds a shared understanding of your business automatically
• A live walkthrough of a real revenue collapse diagnosis: what the database showed, what the analytics showed, what the ad data showed, and how Terno connected them into a single root cause
• A second independent case study demonstrating the same approach on a different business
• How the semantic layer works on what's automated, what benefits from a short guided setup, and why it matters for getting answers you can actually trust
• A live demo: ask Terno a cross-source question and watch it reason in real time

Who should attend:

• Founders and operators who want faster answers from their data without hiring a data team
• Data and analytics leads at companies running on multiple data sources who are tired of reconciling dashboards manually
• Product and growth teams who need to connect acquisition data with product/revenue data to understand what's actually working
• Anyone evaluating AI analytics tools who wants to see what "multi-source reasoning" actually looks like in practice, not just in a slide deck

What Terno is:
Terno is an AI data scientist that connects to every system holding a piece of business truth - your database, your analytics, your ad platforms, and, on the roadmap: your CRM, ERP, logs, and internal documentation. It builds a semantic layer on top of your data automatically, then lets you ask questions across all of it in plain language.
The answers are grounded in real data execution, not AI guesswork.