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
From reporting bottlenecks to faster answers
Reporting problem | What the engine does | Business outcome |
|---|---|---|
Every question waits in the analyst queue | Answers in plain English, inside your real data | Faster answers without adding to the analyst backlog |
Reports rebuilt by hand, every time | Generates reusable answers, saves straight into dashboards | Less repetitive report prep |
Same KPI, five different numbers from teams | Runs on one shared metric definition and reporting logic | Consistent numbers across teams |
Business users can’t explore data themselves | Handles follow-ups and ad-hoc analysis without SQL | Self-service analytics, not self-taught SQL |
Changes and anomalies found too late | Flags patterns with context, as they happen | Earlier investigation and action |
Deploy our AI analytics engine on your existing data
It’s an analytics layer that sits on top of your existing data stack. It doesn’t replace your databases, BI tools, or your product, but uses the data and metric logic you already have. So, you just get and adjust analytical answers in plain language.
- Generates SQL that stays inside your schema
- Validates and logs every output
- Operates within your system rules
- Replace your BI stack
- Act as a general-purpose chatbot on your database
- Generate free-form, unchecked queries
From data to answers in a few steps
Step 1 — Bring in your data
Upload a sample dataset or CSV, or connect a data source. The system prepares it for analysis.
Step 2 — Review the data structure
The engine recognizes the available data structure (tables, columns, basic schema info) and before answering questions.
Step 3 — Ask like you'd ask a colleague
Type the question. Get the answer, the chart, and the reasoning behind it — all grounded in your data.
Step 4 — Keep useful answers
A good answer shouldn’t disappear into chat history. Pin it, add it to a dashboard, keep it working.
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. |
Nothing is a black box
Every question you’ve asked is logged — with the answer, the visualization, and the SQL behind it. If you need to check the logic or reproduce a report, it’s right there.
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
