{
  "id": 9332168,
  "title": "Ekai raises $1.7M to give enterprise AI agents verified business context",
  "url": "https://urgent.news/2026/09/23/ekai-raises-1-7m-to-give-enterprise-ai-agents-verified-business",
  "topic": "ai",
  "section": "AI",
  "published": "2026-09-23T12:00:05.000Z",
  "source": {
    "name": "SiliconANGLE",
    "slug": "siliconangle",
    "url": "https://siliconangle.com/2026/09/23/ekai-raises-1-7m-to-give-enterprise-ai-agents-verified-business-context/"
  },
  "original_language": "en",
  "account": "Enterprise AI startup Ekai Inc. has secured $1.7 million in funding to help businesses provide artificial intelligence agents with verified context. The platform automates the creation of semantic models and data transformation code that enables AI tools to accurately interpret a company's data warehouse. Ekai's unique approach involves working with domain experts to define metrics and terms, which are then translated into machine-readable logic and validation rules. This process, known as forward-engineering, allows AI agents to operate with accurate business context. In early engagements, Ekai claims to have reduced semantic modeling work from weeks to just six hours. The company's investors include early-stage venture fund Misneach and C10 Labs, while co-founders Moatassim \"Mo\" Aidrus, Hussnain Ahmed and Tero Miikki each have over two decades of experience leading technology and data initiatives at major corporations. Ekai's platform now integrates with Snowflake, Databricks, Google BigQuery, Amazon Redshift, Microsoft Azure Synapse, Postgres, ClickHouse and DuckDB.",
  "summary": "Enterprise data startup Ekai Inc. today announced $1.7 million in new funding for software that builds the business context artificial intelligence agents need before they can be trusted with corporate data. Ekai provides a platform that writes the semantic models and data transformation code AI tools rely on to read a company’s data warehouse correctly. […] The post Ekai raises $1.7M to give…",
  "key_points": [],
  "editors_take": null,
  "illustration": null,
  "coverage": {
    "outlets": 1,
    "also_reported_by": []
  },
  "ai_generated": true,
  "disclaimer": "Summaries, key points and the editor’s take are written by software from other outlets’ reporting and may contain errors — always check the linked original."
}