Proprietary data, structured for real-world decisions
mnAi generates and delivers structured datasets across financial, non-financial, and ESG domains - combining depth, coverage, and consistency to support analysis, reporting, and decision-making at scale.
Overview
Data that drives decisions
Data underpins every critical decision - but its value depends on how it is structured, maintained, and applied.
mnAi provides datasets that go beyond traditional sources. By combining multiple data inputs with proprietary processes, we create structured, consistent, and connected datasets that can be used reliably across systems, workflows, and reporting environments.
Structured
Consistent formats
Current
Always up-to-date
Connected
Linked entities
Reliable
Validated data
Data Structure
Three Core Data Pillars
Our data is structured across three key domains
Financial Data
A consistent and structured view of company performance.
- Revenue and financial indicators
- Balance sheet and performance metrics
- Historical financial data
- Standardised outputs across populations
Non-Financial Data
Context beyond financials.
- Company attributes and firmographic data
- Registered and trading addresses
- Directors, officers, and ownership structures
- Operational and structural indicators
- Charges, debt, and financial obligations
- Property and asset-level data
ESG Data
Supporting transparency and reporting.
- Environmental indicators and emissions-related data
- Social and governance metrics
- Structured datasets aligned to reporting frameworks
- Derived ESG indicators and classifications
Clear Definitions
Every field has detailed descriptions and documentation
Standardised Types
Consistent data types across the entire dataset
Defined Values
Clear value ranges and validation rules per entity
Update Frequency
Transparent refresh schedules and applicability
Example Values
Real examples for validation and testing
Data Structure & Design
Engineered for consistency
mnAi datasets are engineered for consistency, usability, and scale. Each data field is meticulously documented to ensure data is not only available - but interpretable, comparable, and reliable across systems.
Data Origination
A multi-layer approach
Our datasets are built through a structured, multi-layer approach
Layer 01
Government Data
Statutory records and official sources.
Layer 02
Societal Data
Publicly available information and broader data signals.
Layer 03
Proprietary Data
Internally developed datasets unique to mnAi.
Layer 04
Derived Data
Attributes generated through proprietary technology and analysis.
Entity Coverage & Lifecycle
Complete entity visibility
mnAi datasets are designed to reflect how organisations operate over time, enabling a more complete and accurate understanding beyond static snapshots.
- Coverage across active and dissolved entities
- Tracking of key corporate lifecycle events
- Visibility into administration, restructuring, and closure
Real-Time & Continuous Updates
Always current
Data is continuously maintained to ensure accuracy and relevance, keeping information current, consistent, and aligned with real-world conditions.
Regular
Scheduled updates
Event-driven
Key changes
Validated
Quality assured
Access Methods
Seamless integration
mnAi data is delivered in a way that integrates seamlessly into your environment
Snowflake
Direct access within your data warehouse
API
Real-time integration into systems and workflows
Flat File
Structured delivery for internal use
Outcome
Structured, consistent, and reliable datasets - designed to support decision-making, reporting, and analysis at scale.