Coginiti provides a secure data operations platform for cleaning, transforming, and modeling data for AI, BI, and operational applications. The platform enhances analytic consistency and productivity through modular development, version control, and collaborative teamwork features. It supports diverse data platforms while enforcing company security policies to deliver reliable data assets confidently.
Funding
$4M raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
Founders
Product
Problem
Organizations struggle to create and maintain trustworthy business logic across diverse data environments, leading to inconsistent metrics, loss of analytical knowledge, and unreliable data for AI initiatives.
Solution
Coginiti offers a Semantic Intelligence platform that enables data teams to develop, test, version, govern, and deploy business logic directly from SQL into an AI‑ready semantic layer. The platform connects to more than 21 database and cloud platforms, supporting cloud, on‑premise, and classified deployments, ensuring a single source of truth across the entire data estate. It captures the full analytic context—including queries, collaboration threads, test results, and assumptions—preserving organizational knowledge and enabling auditable, consistent definitions for downstream AI and analytics workloads. By operationalizing meaning at the semantic layer, Coginiti helps organizations improve data quality, accelerate AI readiness, and maintain governance across heterogeneous environments.
Target Audience
Primary customers are data engineering and analytics teams in large enterprises, government agencies, and defense organizations that need a unified, governed semantic layer to support AI and advanced analytics across complex, heterogeneous data environments.
Features
- End‑to‑end workflow: develop SQL queries, test against live data, version, collaborate, govern, and deploy to a semantic layer within one interface
- Connectivity to 21+ database and cloud platforms, including Snowflake, Databricks, IBM Db2, Oracle, Apache Hive, and support for air‑gapped and classified (IL2–IL6) environments
- Comprehensive knowledge capture that records queries, test results, collaboration threads, assumptions, and version history for each metric
- Centralized governance and audit trails ensuring that AI agents and analytics tools access trusted, consistent business definitions
- AI‑ready semantic layer that abstracts raw column names into curated business concepts, improving data quality for machine‑learning models
- Cloud, on‑premise, and edge deployment options to fit any organizational security and compliance requirements