App Orchid provides an enterprise‑grade semantic context layer powered by a patented knowledge‑graph engine that automatically connects, enriches, and models both structured and unstructured data. The platform delivers near‑100 % accuracy for retrieval‑augmented generation and text‑to‑SQL queries, offering explainable AI answers, auto‑generated visualizations, and secure, role‑based access for large organizations.
Funding
$43M 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
Enterprises struggle to extract accurate, contextual insights from fragmented structured and unstructured data sources, leading to slow decision-making, high AI development costs, and risk of unreliable generative AI outputs.
Solution
App Orchid provides a semantic context layer built on a patented knowledge‑graph engine that automatically connects, enriches, and models enterprise data. By encoding business logic and ontology‑driven schemas, the platform enables near‑100 % accuracy for retrieval‑augmented generation and text‑to‑SQL queries. Users can ask natural‑language questions and receive explainable answers, auto‑generated visualizations, and actionable analytics without needing technical expertise. The solution includes role‑based access controls, encryption, and compliance tracking to ensure secure, trustworthy AI interactions. Integrated connectors to over 150 data stores and support for leading LLMs allow organizations to embed the layer into existing AI pipelines and decision‑intelligence applications.
Target Audience
Primary customers are large enterprises and mid‑market organizations that need enterprise‑wide AI, analytics, or decision‑intelligence capabilities, including data teams, business analysts, and line‑of‑business users across functions such as finance, operations, and customer service.
Features
- Patented knowledge‑graph engine that automatically discovers, connects, and semantically enriches structured and unstructured data
- Ontology‑driven, context‑aware text‑to‑SQL engine delivering industry‑leading query accuracy
- Explainable AI outputs with transparent query code, data lineage, and reasoning for each answer
- Auto‑generated dashboards and visualizations, plus 50+ built‑in data‑science models for quick insights (causal inference, anomaly detection, etc.)
- Fine‑grained role‑based access, built‑in encryption, SSO integration and automated compliance tracking
- Out‑of‑the‑box connectors to 150+ enterprise data sources (SQL, NoSQL, cloud warehouses, SaaS platforms)
- Composable AI architecture supporting integration of external LLMs and custom ML models (e.g., Vertex AI, SageMaker)