Automated data-warehouse builder
Define the business goal.Let Omnia build the warehouse.
Omnia turns business requirements into an implementation plan, then carries data through cleaning, modeling and quality validation to deliver usable data assets.
Project workspace
From a business goal to a usable data model.
A plan built around the business goal, with every step available for review.
A customer data mart, ready to use
Organize source data into dimensions and a business model for downstream work.
| Field preview | Type | Description |
|---|---|---|
| company_id | UUID | Company ID |
| company_name | VARCHAR | Company name |
| status | ENUM | Customer status |
Data assets
One catalog, from source records to business models.
Company profile mart
mart_company_profile
Quality checks
Make the rules behind usable data visible.
One workspace. From task to asset.
Explore the workspace, inspect data structures and review quality rules. This interactive preview follows the product workflow.
You define the business.Omnia handles the data engineering.
Keep business intent at the center. Let a continuous workflow turn it into data you can use.
Define the goal
Describe the business need and confirm sources and expected outputs. Omnia organizes the goal into an actionable implementation plan.
Run the workflow
Progress from cleaning to dimension, fact and metric modeling, followed by quality validation and governance.
Deliver the assets
Bring tables, semantic metrics and governance outputs together as data assets that can be reviewed and handed over for business use.
Use CRM as the master data. Bring in the other sources to complete it.
What should each row represent?
The data model will follow your business definition.
- Confirm the company
Use CRM as the master record.
- Bring the sources together
Clean, standardize and match company data.
- Build the company profile
Produce a table with one company per row.
Technology foundation
Multi-agent reasoning.Connected to execution.
Omnia is built on ID Axis, a multi-agent reasoning framework developed in-house by IDENIFE. It connects model reasoning, task orchestration and tool calls to carry requirements, data processing and result checks through a continuous workflow.
ID Axis
Our multi-agent reasoning framework
A shared goal. Coordinated reasoning across tasks.
Model capabilities
HuanYu
Business understanding and reasoning
Confirmed goals, data sources and quality requirements
Turn task plans into data operations and checks
Data-engineering product
ID Omnia
Tables, semantic metrics and governance outputs
Engineering principles
From business meaningto data structure.
A table’s structure follows the business question it needs to answer. Omnia clarifies entities, sources and delivery requirements, then translates them into data layers, fields and acceptance rules.
Example: building a company profile
Use CRM as the master data. Enrich it with the other sources.
- Raw
CRM and additional sources
- Standardized
Clean, deduplicate and standardize
- Business
Company dimension and profile
| Example field | Acceptance rule |
|---|---|
company_id | Not null · Unique |
company_name | Not null |
fit_grade | Within the agreed value range |
Define grain before relationships
“One company per row” establishes the entity. With CRM as the master, confirm the company identifier and additional sources, then define how tables relate. Company and project records need distinct treatment.
Organize tasks. Build in layers.
ID Axis organizes ingestion, cleaning, standardization and modeling tasks, connecting model reasoning with tool calls. Raw records become consistent structures, then dimensions, facts and metrics for business use.
Make rules the basis for delivery
Turn requirements for nulls, uniqueness and allowed values into quality checks with visible results. Retain table and field lineage so the delivered assets can be examined against the original business requirements.
Built with governance
Every step has a basis.Every asset has a lineage.
Quality and governance are part of the build. Quality rules, table and field lineage, permissions and auditing provide context for the data you deliver and use.
Quality rules
Check for nulls, duplicates and out-of-range values, with explicit validation results.
Data lineage
Explore source relationships between tables and fields to understand how assets were formed.
Permissions & auditing
Include access management and activity records in governance to support data use and oversight.
company_idCompany IDcompany_nameCompany nameindustryIndustrySelect a source to follow its fields into the asset.
ID Omnia
Start your data engineeringwith a business goal.
From requirements to assets, bring a complete data-engineering workflow into your business.
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