# Natural-language analytics query routing

Source: https://altivusops.ai/use-cases/natural-language-analytics-query-routing/
Last modified: 2026-10-13

> Routes a plain-language business question to specialized agents for calculations, product lookups and geographic data, then synthesizes an answer. Access controls scope what each employee can query.

An AI workflow Blueprint — a documented use case for Operations in Manufacturing & Distribution. Complexity: Advanced. Deployment: HeyQuinn recommended.

## The problem

Large consumer goods and manufacturing companies hold enormous regional sales, inventory and promotional data that non-technical marketing and commercial staff cannot reach without a technical team building a custom dashboard. Waiting on that queue means decisions slip while the market opportunity narrows.

- Regional teams cannot query business data without engineering support
- Custom dashboard requests queue up behind other technical work
- A single general-purpose model answering all query types raises hallucination risk
- Access to sensitive regional and product data needs to be scoped per employee

## The solution

An orchestrating agent takes a plain-language business question, routes it to the specialized agent suited to that query type, and returns a synthesized answer with a visualization while people act on the result.

1. Receives a natural-language question from a marketing or commercial team member
2. Determines which specialized agent should handle the query: calculation, product master data, geography master data or raw data retrieval
3. Queries the data warehouse and master data systems through the relevant specialized agent
4. Synthesizes the specialized agents' results into a single answer and visualization
5. Applies identity and access controls so each employee only sees data scoped to their role

## Where people stay in control

- Identity and access controls scope what each employee can query before any answer is returned
- Specialized agents handle narrow query types rather than one general model answering everything, reducing hallucination risk
- Employees interpret and act on the synthesized answer rather than the system taking any automated action

## Systems and data

The workflow reads sales, inventory and promotional data from the internal data warehouse, product identifiers from the product master data system, and regional identifiers from the geography master data system, then writes nothing back, returning synthesized answers and visualizations to the requesting employee.

- data warehouse
- product master data system
- geography master data system

## Results

- Monthly unique users reached 44 thousand generating 2.5 million messages monthly in the documented deployment
- Accuracy on conversational analytics improved from 90 percent with the prior approach to 99.9 percent in the documented deployment

## Why HeyQuinn is recommended

Several specialized agents coordinate under an orchestrator, access is scoped by identity and permissions across sensitive regional and product data, and the deployment runs at large persistent scale, so HeyQuinn is recommended for monitoring and governance.

[What HeyQuinn is](https://altivusops.ai/heyquinn/)

## Frequently asked questions

### Can any employee see all regional data through this system?

No. Identity and access controls scope what each employee can query before an answer is returned.

### Does one model answer every question?

No. An orchestrating agent routes each question to a specialized agent built for that query type, which reduces hallucination risk compared to a single general model.

### What accuracy did this approach reach?

In the documented deployment, accuracy on conversational analytics improved from 90 percent to 99.9 percent compared to the prior approach.

## Related Blueprints

- [Multichannel order intake and ERP conversion](https://altivusops.ai/use-cases/multichannel-order-intake-and-erp-conversion/)
- [Vendor invoice reconciliation](https://altivusops.ai/use-cases/vendor-invoice-reconciliation/)
- [Chargeback claim translation and analysis](https://altivusops.ai/use-cases/chargeback-claim-translation-and-analysis/)

[Discuss this workflow](https://altivusops.ai/strategy-call/?workflow=natural-language-analytics-query-routing)

[Back to the Blueprint library](https://altivusops.ai/use-cases/)

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