AI analytics engine
No need to rebuild anything. We work with your existing data environment, define key metrics, and let our AI analytics engine generate explainable reports with ready-to-use insights.
What do you want to fix in your reporting?
Deliver metrics in minutes, not weeks
Remove manual exports and slide preparation
Let business teams adjust reports without SQL
Detect risks and anomalies automatically
Eliminate conflicting KPI definitions
Establish a single, trusted metric logic
Deploy our AI analytics engine on your existing data
A governed AI reporting layer added on top of your existing data model.
It translates defined business metrics into validated, traceable outputs – so teams can request and adjust reports in plain language without writing SQL or waiting on ad-hoc queries.
What pluses?
- Built on a defined semantic model
- SQL generation restricted to your schema
- Validated and logged outputs
- Operates within your existing system rules
Good “Not” points
- Not a chatbot on top of your database.
- Not another dashboard tool.
- Not free-form AI generating unchecked queries.
Not BI. Not a chat assistant. So where does it fit?
Different tools solve different reporting tasks. Here is how this layer compares to BI tools and generic AI models.
If dashboards are enough, use BI.
If general answers are enough, use an AI assistant.
If you need controlled reporting on live business data, use our AI analytics engine.
One engine – two deployment paths
Embed it into your product
Add governed AI reporting directly into your SaaS or platform. Keep your UI, your workflows, your database.
Run it as a standalone solution
Deploy it as an independent reporting layer that connects to your data sources and delivers validated outputs.
Can AI reporting be trusted?
Only if guardrails are built in from the start. Ours are.
Prevent invalid queries and hallucinated SQL
AI is limited to registered schemas and documented fields. SQL is parsed and constrained before execution to block undefined or unrestricted queries.
Validate insights before delivery
Outputs pass deterministic validation before delivery. Sanity checks verify structure, ranges, and row counts to keep reports explainable.
Protect sensitive data before AI processing
Sensitive data is controlled before AI processing. PII masking, redacted logs, and tenant isolation reduce exposure risk.
Ensure full traceability and audit readiness
All reporting activity is traceable and reproducible. Versioned prompts, audit logs, and read-only access support governance and audits.
How the AI analytics engine is implemented
The module is introduced through a structured implementation process that aligns your data model, metric definitions, and access rules before AI-generated reporting is activated.
1
Data landscape assessment →
Your schemas, reporting flows, and KPI definitions are reviewed to identify gaps and inconsistencies before implementation.
2
Semantic layer and metric alignment →
A governed schema registry and stable KPI definitions are established to ensure consistent reporting logic.
3
AI reporting layer integration →
The controlled NL→SQL engine is introduced into your environment with defined query constraints, validation rules, and logging.
4
Delivery and interface setup
Reports are exposed via APIs or embedded tools with configured access control and full traceability.
How it connects and delivers
Pick your inputs, set the outputs, and let the AI analytics engine do its job.
Inputs we support
- CSV exports (start immediately)
- Postgres, MySQL, BigQuery, Snowflake
- Product databases or event streams
Outputs you control
- REST endpoints for programmatic access
- WebSocket streaming for live metrics
- Embed-ready dashboards inside your product or portal
- Automated PDF / CSV exports for boards, audits, QBRs
- Optional push of derived metrics into existing BI tools
