Conversational AI analytics on large-scale MongoDB data.

Business users ask questions in plain English and get governed, accurate answers from large MongoDB datasets, without writing a query.

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Client: Finn Design
Industry: eCommerce Analytics / SaaS
Delivery: AI-driven analytics platform
Capability: Data & Analytics

The challenge

What Finn Design was up against.

Finn Design’s eCommerce and advertising data was complete but locked behind complex MongoDB queries, schema knowledge and multiple dashboards.

Large datasets across many collections
No natural-language interface
Complex aggregation pipelines
No conversational follow-ups
Heavy dependence on engineers

The solution

What we built.

Skugal built a conversational analytics layer using MCP-style orchestration that turns questions into safe, optimised MongoDB queries and explains the results.

Conversational interface

Ask “Show me yesterday’s total sales” or “Compare this month vs last month”, with follow-ups and context.

AI-generated queries

Chooses collections, find or aggregate, and builds read-only, validated pipelines.

Tool-based execution

Queries run only through approved tools; no raw database access is ever exposed to the model.

Multi-collection intelligence

Combines sales, advertising and order data to answer complex questions in business language.

How it works

From input to outcome.

User question
Intent & context handling
AI-generated MongoDB query
Governed tool execution
Result interpretation
Conversational answer

Business impact

What changed.

Finn Design now has a production-ready conversational analytics platform that makes its data accessible to everyone while preserving accuracy and governance.

Technology