Connecty AI is an Agentic AI analytics platform that lets you ask complex business questions in natural language and receive accurate, context-aware, and explainable insights. It combines deep reasoning with an autonomous semantic graph to deliver consistent, reliable analytics without manual SQL or dashboard modeling. Connecty AI connects directly to your cloud data warehouse using no-code setup, automatically discovers and maintains metric logic, and enables AI-driven analysis across your organization. It empowers both business and technical users to explore data, uncover insights, and ensure metric consistency - all through trusted, self-service natural language queries.
Connecty AI is designed for data analysts, analytics engineers, business analysts, data governance, data leaders and business leaders who need fast, accurate insights from enterprise data - without relying on manual SQL or predefined dashboards. It is also useful for business teams looking for trusted self-service analytics.
Connecty AI supports a wide range of business and analytical questions focused on metric lookups, time-based aggregations, comparisons, segmentation, drilldowns, anomaly or trend detection, and funnel or drop-off analysis. You can ask questions like “What is the total revenue in Q1 2024?”, “Compare revenue this year vs last year”, or “List high-value customers by region.” See more examples. These questions can include filters, breakdowns, and complex multi-step logic when Deep Reasoning Mode is enabled, allowing you to handle even detailed, layered analyses in natural language.
Connecty AI does not support questions that require external or real-time data, such as weather updates, stock prices, or industry benchmarks that are not stored in your database. It also does not handle system-level questions, sentiment or opinion analysis outside your data, or general business advice. All supported queries rely strictly on the data available in your connected data warehouses to ensure accurate, secure, and context-aware responses.
Yes. Although Connecty AI does not use external data sources (like weather data or stock prices), it understands market-standard definitions of common business metrics, such as CLTV (Customer Lifetime Value), AOV (Average Order Value), and others. It can extrapolate business context and automatically suggest or infer these metrics, even if they are not explicitly defined in your data warehouse. This helps ensure consistent, trusted analytics aligned with industry standards - without needing to manually define every metric in advance.
No. Connecty AI automatically builds an Autonomous Semantic Graph, learning metric definitions, relationships, and business logic directly from your data. There is no need to set up or maintain a manual semantic layer.
Connecty AI stands out as an Agentic AI data analytics platform with contextual intelligence, deep reasoning, and an autonomous semantic graph, going far beyond traditional text-to-SQL bots or rigid BI tools. Unlike Databricks AI/BI Genie, Connecty AI offers plug-and-play no-code setup, auto-syncing catalogs, and self-updating semantic graphs that support multiple data warehouses and Hive Metastore environments out of the box. Its Intent Graph architecture enables autonomous deep reasoning on multi-layered questions, contextual follow-ups, and traceable, versioned answer history — not limited, one-shot queries. Connecty AI provides fully autonomous metric definition, discovery, and reuse, including conflict resolution and change management without manual modeling. Business logic scales automatically through a self-healing, versioned graph with actionable explainability and visual editing. Additionally, Connecty AI supports unlimited tables, views, schemas, and instructions, with high throughput and no rate limits — ensuring enterprise scalability without performance bottlenecks. In short, Connecty AI combines advanced autonomous reasoning, dynamic semantic governance, and unmatched scalability to deliver accurate, trusted analytics at enterprise scale — far beyond traditional AI SQL generators or static BI dashboards.
Connecty AI supports plug-and-play integrations with the following data warehouses:
Each Data Environment in Connecty AI supports one active connection. However, you can create multiple Data Environments, and each can have a different connection to a specific data source or warehouse. This allows you to work with multiple data sources in parallel while keeping environments isolated and clearly managed.
No. Connecty AI uses a data-in-place architecture, so all processing happens directly within your data warehouse (e.g., Snowflake, Databricks, BigQuery, PostgreSQL). There is minimal and fully controlled data movement, with no need to copy or duplicate data outside your environment.
Connecty AI provides full control over every interaction with your data, including query execution and sampling for its Context engine. Actions can be disabled or set to require explicit confirmation. It operates solely through service account access, so permissions and policies are managed within your infrastructure. From the moment data is connected, Connecty AI automatically extracts PII-related metadata and infers additional context, with the option to override these inferences. No sensitive data is exposed to large language models (LLMs). Key security features include:
Connecty AI uses a plug-and-play, no-code integration framework that takes about 10–15 minutes to connect, sync, and enrich your data catalog. You can start analyzing and generating insights immediately, without writing any code or waiting for long setup processes. This approach helps teams focus on insights rather than integration work, and eliminates the need for heavy IT involvement.
Currently, Connecty AI supports cloud data warehouses only, including Snowflake, BigQuery, PostgreSQL, and Databricks. These warehouses can be hosted on any cloud platform (such as Azure, AWS, or GCP). On-premises and hybrid environments are not supported at this time.
manual SQL or dashboard modeling. Connecty AI connects directly to your cloud data warehouse using no-code setup, automatically discovers and maintains metric logic, and enables AI-driven analysis across your organization. It empowers both business and technical users to explore data, uncover insights, and ensure metric consistency - all through trusted, self-service natural language queries.
Connecty AI is designed for data analysts, analytics engineers, business analysts, data governance, data leaders and business leaders who need fast, accurate insights from enterprise data - without relying on manual SQL or predefined dashboards. It is also useful for business teams looking for trusted self-service analytics.
Connecty AI supports a wide range of business and analytical questions focused on metric lookups, time-based aggregations, comparisons, segmentation, drilldowns, anomaly or trend detection, and funnel or drop-off analysis. You can ask questions like “What is the total revenue in Q1 2024?”, “Compare revenue this year vs last year”, or “List high-value customers by region.” See more examples. These questions can include filters, breakdowns, and complex multi-step logic when Deep Reasoning Mode is enabled, allowing you to handle even detailed, layered analyses in natural language.
Connecty AI does not support questions that require external or real-time data, such as weather updates, stock prices, or industry benchmarks that are not stored in your database. It also does not handle system-level questions, sentiment or opinion analysis outside your data, or general business advice. All supported queries rely strictly on the data available in your connected data warehouses to ensure accurate, secure, and context-aware responses.
Yes. Although Connecty AI does not use external data sources (like weather data or stock prices), it understands market-standard definitions of common business metrics, such as CLTV (Customer Lifetime Value), AOV (Average Order Value), and others. It can extrapolate business context and automatically suggest or infer these metrics, even if they are not explicitly defined in your data warehouse. This helps ensure consistent, trusted analytics aligned with industry standards - without needing to manually define every metric in advance.
No. Connecty AI automatically builds an Autonomous Semantic Graph, learning metric definitions, relationships, and business logic directly from your data. There is no need to set up or maintain a manual semantic layer.
Connecty AI stands out as an Agentic AI data analytics platform with contextual intelligence, deep reasoning, and an autonomous semantic graph, going far beyond traditional text-to-SQL bots or rigid BI tools. Unlike Databricks AI/BI Genie, Connecty AI offers plug-and-play no-code setup, auto-syncing catalogs, and self-updating semantic graphs that support multiple data warehouses and Hive Metastore environments out of the box. Its Intent Graph architecture enables autonomous deep reasoning on multi-layered questions, contextual follow-ups, and traceable, versioned answer history — not limited, one-shot queries. Connecty AI provides fully autonomous metric definition, discovery, and reuse, including conflict resolution and change management without manual modeling. Business logic scales automatically through a self-healing, versioned graph with actionable explainability and visual editing. Additionally, Connecty AI supports unlimited tables, views, schemas, and instructions, with high throughput and no rate limits — ensuring enterprise scalability without performance bottlenecks. In short, Connecty AI combines advanced autonomous reasoning, dynamic semantic governance, and unmatched scalability to deliver accurate, trusted analytics at enterprise scale — far beyond traditional AI SQL generators or static BI dashboards.
Connecty AI supports direct integrations with the following data warehouses:
Each Data Environment in Connecty AI supports one active connection. However, you can create multiple Data Environments, and each can have a different connection to a specific data source or warehouse. This allows you to work with multiple data sources in parallel while keeping environments isolated and clearly managed.
No. Connecty AI uses a data-in-place architecture, so all processing happens directly within your data warehouse (e.g., Snowflake, Databricks, BigQuery, PostgreSQL). There is minimal and fully controlled data movement, with no need to copy or duplicate data outside your environment.
Connecty AI provides full control over every interaction with your data, including query execution and sampling for its Context engine. Actions can be disabled or set to require explicit confirmation. It operates solely through service account access, so permissions and policies are managed within your infrastructure. From the moment data is connected, Connecty AI automatically extracts PII-related metadata and infers additional context, with the option to override these inferences. No sensitive data is exposed to large language models (LLMs). Key security features include:
Connecty AI uses a plug-and-play, no-code integration framework that takes about 10–15 minutes to connect, sync, and enrich your data catalog. You can start analyzing and generating insights immediately, without writing any code or waiting for long setup processes. This approach helps teams focus on insights rather than integration work, and eliminates the need for heavy IT involvement.
Currently, Connecty AI supports cloud data warehouses only, including Snowflake, BigQuery, PostgreSQL, and Databricks. These warehouses can be hosted on any cloud platform (such as Azure, AWS, or GCP). On-premises and hybrid environments are not supported at this time.
