What platforms does Coginiti support?

Coginiti is available on Web App, Windows, MacOS and Linux.

What are the limitations of Coginiti?

No pricing information is published on the homepage, so cost has to be established through a sales conversation. The homepage lists database connectivity but does not name BI tools, catalogs or AI agent frameworks that consume the semantic layer. A SQL-first workflow assumes an analytics engineering team comfortable writing and reviewing queries; less technical stakeholders will not self-serve.

Coginiti

Name: Coginiti
Author: Coginiti

Turn tested SQL into a governed, AI-ready semantic layer

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Screenshot of Coginiti, Turn tested SQL into a governed, AI-ready semantic layer

What is Coginiti?

Coginiti is an analytics tool that turns tested SQL into a governed, AI-ready semantic layer. A semantic intelligence platform that takes data teams from SQL development and testing through versioning and governance to a deployable semantic layer, with connections to 21 or more database platforms, including on-premises, air-gapped and classified environments.

Coginiti is a Semantic Intelligence platform built around a simple idea: the semantic layer should be the last step of analytic work, not the first. Teams begin in the Data Workbench writing SQL, run queries against real data, version and review the logic, and only then promote the vetted definition into a governed semantic layer. The platform is positioned against tools that start at metric definition and leave the surrounding reasoning behind. The product spans the full lifecycle of business logic: develop, test, version, govern, deploy. Because work starts in SQL and ends in a governed layer, the queries, test results, collaboration threads, assumptions and operational context stay attached to the definitions that reach production. Coginiti frames this as organizational knowledge capture, a way to retain what analysts knew after the people who built the logic have moved on. Connectivity is the second pillar. Coginiti states support for 21 or more database platforms, which it claims is the broadest in the industry, ranging from Snowflake, Databricks, IBM Db2, Oracle and Apache Hive to commercial cloud, on-premises, edge and air-gapped networks, including IL2 to IL6 classified environments. That breadth explains the customer roster, which mixes government and defense bodies such as US NAVSUP, the US Army Corps of Engineers and DHS CISA with commercial names like CVS Health and O'Reilly Auto Parts. For AI programs, the pitch is grounding over inference. When an AI agent consults the semantic layer it queries governed definitions rather than raw column names, so answers are auditable and consistent with what analysts already treat as true. The tool is aimed at data engineering and analytics teams in regulated or heterogeneous estates, and at organizations whose AI initiatives depend on data quality. It competes with semantic layer and analytics engineering products, differentiated by its SQL-first workflow and classified environment support rather than by metric modeling alone.

How do you use Coginiti?

1Book a guided walkthrough
Start by requesting the Semantic Intelligence demo, where the team shows the develop, test, version, govern and deploy flow against a real data estate.
2Read the category blueprint
The blueprint page explains how Coginiti frames semantic intelligence and where it positions itself relative to pure semantic layers. Useful before any internal evaluation.
3Connect your database platforms
Inside the product, configure connections to the platforms in your estate, whether that is Snowflake, Databricks, Oracle, Db2 or an air-gapped system.
4Write and test queries in the Data Workbench
Develop SQL against live data and run it to validate results before anything is promoted. Test outcomes stay attached to the work.
5Version, review and govern
Bring stakeholders into the review thread, capture assumptions, and version the logic so changes are traceable across the team.
6Promote logic into the semantic layer
Publish vetted definitions to the governed layer so analysts and AI agents query trusted business logic instead of raw column names.

Pros and cons

Pros

Workflow starts in SQL and carries tests, versions and context through to a governed semantic layer, rather than beginning at metric definitionAI
Connects to 21 or more database platforms, including IBM Db2, Oracle, Hive and air-gapped or IL2 to IL6 classified networksAI
Governed definitions give AI agents a curated grounding layer, so answers are auditable and consistent with analyst knowledgeAI
Public sector and enterprise references include US NAVSUP, the US Army Corps of Engineers, DHS CISA and CVS HealthAI
Captures queries, collaboration threads and test results alongside definitions, which helps when the original authors leaveAI

Cons

No pricing information is published on the homepage, so cost has to be established through a sales conversationAI
The homepage lists database connectivity but does not name BI tools, catalogs or AI agent frameworks that consume the semantic layerAI
A SQL-first workflow assumes an analytics engineering team comfortable writing and reviewing queries; less technical stakeholders will not self-serveAI
The value case rests on a governed layer, which means heavier setup and process discipline than lighter metric definition toolsAI
Detailed documentation, deployment options and support terms are not visible from the marketing site and require a demo requestAI

How much does Coginiti cost?

Integrations

The homepage names 21 or more database platforms, including Snowflake, Databricks, IBM Db2, Oracle and Apache Hive, plus support for commercial cloud, on-premises, edge and air-gapped networks and IL2 to IL6 classified environments.

Features

Covers a SQL Data Workbench for developing and testing queries against real data, versioning and stakeholder collaboration, governance and deployment of trusted logic into a semantic layer, connections to 21 or more database platforms across cloud, on-premises, edge and classified environments, and knowledge capture that keeps queries, tests, assumptions and context with each definition.

Learn more

Frequently asked questions about Coginiti

How do you use Coginiti?
The walkthrough on this page covers 6 steps: 1. Book a guided walkthrough 2. Read the category blueprint 3. Connect your database platforms 4. Write and test queries in the Data Workbench 5. Version, review and govern 6. Promote logic into the semantic layer.
What platforms does Coginiti support?
Coginiti is available on Web App, Windows, MacOS and Linux.
What does Coginiti integrate with?
The homepage names 21 or more database platforms, including Snowflake, Databricks, IBM Db2, Oracle and Apache Hive, plus support for commercial cloud, on-premises, edge and air-gapped networks and IL2 to IL6 classified environments.
What are the limitations of Coginiti?
No pricing information is published on the homepage, so cost has to be established through a sales conversation. The homepage lists database connectivity but does not name BI tools, catalogs or AI agent frameworks that consume the semantic layer. A SQL-first workflow assumes an analytics engineering team comfortable writing and reviewing queries; less technical stakeholders will not self-serve.