# Teramot

> Teramot connects any AI model with your company's real data. In one hour. No technical team required.

Teramot is a B2B SaaS data infrastructure platform that enables companies to build AI-ready data pipelines without engineering resources. It automatically connects to any data source (databases, spreadsheets, APIs, CRMs) and transforms raw data into clean, structured tables that AI models can query directly via MCP (Model Context Protocol).

## Core capabilities

- **Automated ETL**: Bronze → Silver → Gold pipeline generation powered by AI
- **MCP connections**: Native Model Context Protocol support for ChatGPT, Claude, Gemini and other AI models
- **AI SQL generation**: Natural language to SQL for business analysts
- **SOC 2 Certified**: Read-only access, audited annually
- **No-code setup**: Operational in under one hour, no engineering team needed

## Who uses Teramot

Mid-market and enterprise companies in Latin America and globally, including regulated industries (insurance, finance, healthcare). Used by data analysts, business intelligence teams, and executives who need AI to understand their company data.

## Key pages

- Homepage: https://teramot.com/
- Pricing: https://teramot.com/pricing.html
- Documentation: https://teramot.com/docs.html
- Insights (blog): https://teramot.com/blog
- App: https://app.teramot.com/

## Contact

- Schedule a call: https://calendar.google.com/calendar/appointments/schedules/AcZssZ33FnFB6C77TpwVa_Q_2dGDqIKtScwvYgM6v-zdmIwIQc9wfCIqyWIONocFwe5XwbWnsxJajgUE

## Insights

### Data Is Not Software — June 2026
URL: https://teramot.com/blog/data-is-not-software
Author: Bruno Ruyú, Co-founder & CEO at Teramot

Our read on what Anthropic's AI analytics project reveals about the real challenge of getting AI to work on business data — and why most companies can't replicate it alone.

This week, Anthropic published a detailed account of how they built self-service data analytics internally using Claude. It's an unusually candid piece. Not because it showcases what Claude can do, but because it documents what it actually took, and where the limits are.

The company that builds the most capable AI models in the world needed months of specialized engineering work to answer their own internal business questions reliably. Not because Claude isn't powerful. But because the problem isn't a model problem. It's a data problem.

LLMs alone can't solve it — because data is not software. Code is deterministic and self-describing. Data carries business context, organizational history, ambiguity, and constant change that accumulates over years. A model cannot infer what "revenue" means in your specific context, or why two departments calculate the same metric differently.

Anthropic tested this directly: when they tried to auto-generate their semantic layer using LLMs, the output was "net-negative on our evals versus a smaller, human-curated layer."

After significant engineering investment, they report 95% accuracy in production — and are candid that they don't yet have a robust solution for "silent failures": answers that are wrong, look plausible, and get used without anyone noticing.

Teramot's approach: start one layer below. Deploy a full data lakehouse (Bronze, Silver, Gold) where every transformation is deterministic SQL and all metadata lives in a structured relational database. Once that foundation is in place, the AI doesn't infer — it queries. A company can go from zero to a governed, AI-ready data lakehouse by signing up and entering a credit card. No onboarding. No specialized engineers required.