DataMind Enterprise

On-premise enterprise AI analytics and reporting — turn internal data into trusted answers, dashboards, and scheduled insights with full governance and auditability.

Generic data-source engine
Schema sync and semantic catalog
Multi-level security
Query Plan first

Read the full story ↓

01 / 14

DataMind Enterprise is an on-premise AI analytics and reporting platform for organizations that want artificial intelligence on internal data while keeping strong control over security, permissions, deployment, and auditability.

Overview

On-premise enterprise AI analytics and reporting — turn internal data into trusted answers, dashboards, and scheduled insights with full governance and auditability.

Installed inside the customer environment — not as a central SaaS data pool. Administrators connect approved sources, map technical tables into business-friendly entities, define relationships and security rules, and publish validated catalogs. Business users ask questions in natural language and receive summaries, tables, charts, KPI cards, Excel/PDF/Word exports, emails, and scheduled reports.

DataMind is not only an AI chat interface. It is a controlled enterprise data intelligence layer that prepares, secures, validates, and governs data before AI is allowed to answer — with the AI never acting as the security boundary or connecting directly to databases.

Executive overview

Secure by design: the backend validates permissions, query plans, SQL safety, masking, and exports — AI is never the security boundary. On-premise deployment keeps operational data under customer control. A business semantic layer translates technical schema into domains and entities. AI-powered reporting through chat, charts, KPIs, exports, and scheduled insights.

Open resource

Business problem & vision

Organizations have valuable data across ERP, finance, insurance, and legacy systems — but struggle with accessibility, meaning, permissions, and speed. DataMind introduces a governed lifecycle: connect, scan, map, secure, validate, and publish before users query — turning AI from a risky free-form tool into a controlled analytical assistant.

End-to-end user journey

Administrators install on-premise, configure licensing, add data sources, test connections, sync schema, map business catalog, define security rules, configure AI prompts, validate, and publish. Business users log in, ask natural-language questions, receive allowed semantic context, get Query Plans validated by the backend, and see summaries, tables, charts, or exports — all audited.

Data source connection engine

Not tied to one customer schema. Each connector type exposes dynamic configuration metadata rendered by the frontend. Configurations are stored encrypted. Sources progress through Draft → Connection Tested → Schema Synced → Configured → Security Configured → Published (or Disabled/Error).

Schema sync & data catalog

After connection testing, DataMind scans technical schema and builds a semantic layer — e.g. table HPOLICY becomes entity Policies, column HPOL_PREMTL becomes Premium Amount. Business users see understandable names while technical complexity stays hidden from AI prompts.

Security, permissions & masking

Permissions at data source, domain, entity, field, row filter, output format, and system-action levels. Deny overrides allow. Sensitive fields can be hidden, masked, hashed, or role-restricted. Row filters are injected by backend services; every query, export, and admin change is logged.

AI chat & analytical outputs

Designed for business analysis, not uncontrolled SQL. Users ask questions; AI helps build a structured Query Plan; the backend validates and executes safely. Outputs include text summaries, tables, charts, KPI cards, Excel/PDF/Word exports, email delivery, and scheduled reporting per allowed permissions.

Architecture & deployment

API-first modular monolith: React SPA portal, central .NET API, separated modules (identity, licensing, catalog, security, AI, query engine, chat, reports, exports, scheduler, audit), infrastructure for system DB, connectors, file storage, AI adapters, and background workers. Customer data sources remain in the customer environment via read-only connectors.

Secure Query Plan model

AI never receives database credentials or connects directly. Only allowed domains, entities, fields, and relationships enter prompt context. Backend query builders generate dialect-specific SQL from approved plans with mandatory security filters, result limits, masking, and export checks — every question, plan, SQL, row count, and status is audited.

Business value

Executives: faster strategic indicators and governed analytics without manual report cycles. Analysts: natural-language access to published catalogs, reusable reports, and scheduled outputs. IT & security: on-premise model, read-only execution, encrypted configs, extensible connectors, and audit trails covering questions, plans, SQL, exports, and schedules.

Key features

Generic data-source engine

SQL Server, Oracle, PostgreSQL, MySQL, REST APIs, Excel, CSV — dynamic connector forms and encrypted configuration.

Schema sync and semantic catalog

technical tables/columns mapped to business domains, entities, measures, and dimensions.

Multi-level security

source, domain, entity, field, row filters, output/export permissions, and sensitive-field masking.

Query Plan first

AI proposes structured plans; backend validates, injects filters, builds safe read-only SQL, and audits execution.

AI chat outputs

text summaries, tables, charts, KPI cards, Excel/PDF/Word exports, and email delivery per permissions.

Reports, scheduling, and audit

saved templates, scheduled runs, export history, and full query/export/admin logs.

On-premise deployment

independent installation per customer with local control of infrastructure and data sources.

Modular monolith

Identity, Licensing, Data Sources, Catalog, Security, AI Orchestration, Query Engine, Chat, Reports, Exports, Scheduler, Audit.

Pricing & plans

For the Sultanate of Oman, DataMind implementation or subscription packages typically start from OMR 1,000. Final pricing depends on connected data sources, user and role counts, deployment model (on-premises vs private cloud), optional voice, integrations, and support tier.

Contact UnityCores for a scoped quote aligned with your governance, data residency, and security requirements.

—

Links & resources

Official links for this project.

Video walkthrough

AI Motor-Claims Analysis

Talk to the team

Have a system to build?

Tell us the problem. An engineer — not a salesperson — replies within one working day.

Info@unitycores.com WhatsApp +968 9858 8530