Terno AI

The Problem

Most business users cannot get useful analytical answers from their own data.

The data exists. The databases are there. But obtaining a real analytical answer — not a summary, not a visualization, but an actual answer to a real business question — requires a data analyst, a specific query, and significant time.

Dashboards help with reporting. But they do not help when the question is new, when the analysis is complex, or when you need to combine multiple data sources in an unexpected way.

There are now AI tools that let users ask questions in natural language. Most of them generate SQL or text. But they face a fundamental problem:

When a language model answers a question about data, it is generating text that sounds like the right answer. That is not the same as performing analysis.

For business decisions, plausible-sounding is not enough. You need correct.

What Terno AI Does Differently

Terno AI does not guess answers. It generates and executes analytical code.

When you ask Terno a question, it creates a verifiable analytical workflow — one that actually runs against your data. The result is computed, not composed. You can inspect the code. You can reproduce the result. You can verify the logic.

Enterprises do not need AI that guesses answers from their data. They need AI that performs analysis correctly, securely and reproducibly.

What Terno Can Analyse

Built for Enterprise Security

Connecting AI to enterprise data requires more than capability. It requires trust.

• ✓
  Data stays in your environment Terno AI can be deployed within your private cloud or on-premises infrastructure. Your data does not have to leave your security perimeter.
• ✓
  Code-based, verifiable analysis Every answer is backed by executable code that you can inspect, audit and reproduce. No black-box outputs.
• ✓
  Sandboxed execution Analytical code runs in a secure, isolated environment. There is no pathway from analysis to unintended system access.
• ✓
  Semantic layer for accuracy Terno maintains a structured understanding of your data — what tables mean, how columns map to business concepts, which terms users use. This makes answers dramatically more reliable.
• ✓
  Access controls and permissions Fine-grained controls determine which users can query which data, down to row and column level. Security is deterministic, not model-dependent.

The Semantic Layer

Real enterprise databases are messy. Table names were chosen by engineers, not business users. Column names are abbreviations or legacy codes. Business concepts are undocumented. The relationship between what a user means by "active customer" and what the database actually contains requires knowledge that is not in the data itself.

Terno AI addresses this through a semantic layer: a maintained, structured understanding of what the data actually means — what tables represent, how columns map to business concepts, what business formulas mean, which terms users commonly use and what they refer to.

This is the difference between an AI that occasionally produces plausible-looking answers and one that reliably produces correct ones.

Why I Am Building This

I have spent years at Amazon and InMobi working on systems where data drives decisions at scale. I have seen how much time and expertise is required to turn data into useful insight — even in organisations that have excellent engineering and analytics teams.

The promise of AI-powered analytics is real. But most current tools substitute the appearance of insight for the reality of it. I am building Terno AI because businesses deserve better — an AI data scientist that is secure, correct and genuinely useful for real analytical work.