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.

BI tools
- Best for fixed dashboards and recurring reports
- New questions often require analyst support or dashboard updates
- Strong control within predefined views
- Typically operate as a separate reporting environment
Generic AI models
- Best for open-ended questions and quick exploration
- Can answer anything, but not always grounded in your data
- Limited control without additional safeguards
- Usually used as standalone assistants
Our analytics layer
- Built for governed, on-demand reporting on real business data
- Questions asked in plain language within your schema and metrics
- Outputs are validated, traceable, and consistent
- API-first, works inside your product and existing stack

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.

What it looks like in real work

Different roles. Same reporting foundation. Clear decisions without metric debates.

Every Monday, my leadership team receives a structured executive brief summarizing revenue movement, CAC shifts, and pipeline risk – with clear explanations of what changed and why. We spend time making decisions, not reconciling numbers.”

AliceCFO
When SLA performance starts drifting, we see it before customers complain. I get a clear summary of bottlenecks and impacted workflows – early enough to act.”

DavidHead of Operations
I can track feature adoption by cohort and immediately see how it affects retention and revenue. Product discussions are grounded in measurable impact, not assumptions.”

MichaelProduct Owner
If something unusual happens – a revenue spike, usage drop, irregular pattern – it’s flagged with context. We don’t just see that something changed; we understand where to look.”

SaraRisk Manager
When auditors ask for a report, we reproduce it quickly with the exact logic used originally. No rebuilding, no manual tracing – everything is documented.”

ThomasCompliance Lead
Client reporting is consistent across tenants. We don’t build custom extracts anymore – updates are structured, repeatable, and ready for QBRs.”

ElenaCustomer Success Director

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.

Data landscape assessment →

Your schemas, reporting flows, and KPI definitions are reviewed to identify gaps and inconsistencies before implementation.

Semantic layer and metric alignment →

A governed schema registry and stable KPI definitions are established to ensure consistent reporting logic.

AI reporting layer integration →

The controlled NL→SQL engine is introduced into your environment with defined query constraints, validation rules, and logging.

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