SirDash.ai provides an enterprise AI platform that translates natural language queries into database insights, eliminating the need for SQL. The system securely connects to various data sources, offering a unified access layer for both technical and non-technical users. This conversational data intelligence accelerates analysis workflows and enables data-driven decision-making across organizations.
Funding
Funding not disclosed
Founders
Product
Problem
Organizations often rely on specialized SQL expertise to extract insights from relational databases, creating bottlenecks and delaying decision‑making. Data silos across PostgreSQL, SQL Server, Oracle, and other systems further impede cross‑database analysis, while strict security and governance requirements limit broader access to data.
Solution
SirDash.ai delivers an enterprise AI platform that translates natural‑language questions into precise SQL statements, enabling users to query any connected database without writing code. A domain‑aware Retrieval‑Augmented Generation (RAG) engine combines large‑language‑model reasoning with a built‑in semantic layer that maps table schemas and relationships, producing optimized queries that include automatic joins and filters. Results are returned instantly and can be visualized or exported through interactive dashboards, while continuous conversational refinement improves accuracy over time. The platform enforces role‑based access controls, TLS 1.3, AES‑256 encryption, and supports on‑prem, private VPC, or air‑gapped deployments to meet enterprise security and compliance standards. By providing a unified data access layer, SirDash.ai accelerates analytics for product, sales, and data‑science teams, reducing reliance on data engineers and shortening insight cycles.
Target Audience
Primary users are product managers, data analysts, data scientists, and sales/operations teams in data‑intensive enterprises such as telecom, finance, and e‑commerce that need rapid, self‑service analytics without SQL expertise.
Features
- Natural‑language query interface that generates adaptive, production‑grade SQL for PostgreSQL, Microsoft SQL Server, Oracle, and future connectors
- Domain‑aware Retrieval‑Augmented Generation (RAG) model that incorporates organization‑specific schema knowledge to produce context‑rich answers
- Built‑in semantic data layer that automatically discovers relationships, resolves joins across tables, and enables cross‑database insights
- Interactive refinement loop allowing users to iteratively adjust queries in conversation, with the system learning from feedback
- Enterprise‑grade security: end‑to‑end TLS 1.3, AES‑256 at‑rest encryption, granular RBAC, row/column‑level policies, SSO (Okta, Azure AD, Google Workspace)
- Deployment flexibility: SaaS, private VPC, on‑prem Docker‑isolated tenant, or air‑gapped installations with customer‑managed credential vaults
- Integrated visualization tools for ad‑hoc charts, cohort analysis, and anomaly detection, plus exportable reports for downstream modeling
- Audit‑ready logging, SIEM integration, and compliance roadmaps for SOC 2 Type II, ISO 27001, GDPR, and HIPAA environments