The ERP vendor's customers needed answers, not reports. We built the layer that writes the SQL.
Every question meant a ticket to the reporting team. Neuramonks built a natural language ERP analytics tool that reads the database schema, generates SQL, and returns live answers without the model ever touching the data.
Delivered for the ERP vendor
- 50%
- Less manual reporting
- Schema
- All the model sees
- Live
- Straight from the ERP
- SQL
- Generated per question
Delivered for the L&D team
- 50%
- Less manual reporting
- Schema
- All the model sees
- Live
- Straight from the ERP
- SQL
- Generated per question
The Client's Problem
Every question was a ticket to the reporting team.
- Reports queued behind an analyst
- A question answered in the moment
- Decisions made on last week's export
- Answers straight from live ERP data
- Analytics tools needing a copy of the data
- The model reads the schema, never the records
What We Delivered
Six pieces of work, one query layer.
- 01 · Agentic AI
- Natural-language query agent
- Turns a business question into an executable SQL statement.
- 02 · Data Architecture
- Schema-only integration
- The model reads structure, never the underlying records.
- 03 · MCP Engineering
- Secure tool layer
- Queries run inside boundaries the client defines.
- 04 · Applied AI
- Query validation
- Generated SQL is checked before it reaches the database.
- 05 · Product Engineering
- Self-service analytics UI
- Business users ask and read results without writing SQL.
- 06 · Deployment
- Multi-tenant rollout
- One integration across every customer of their ERP.
- Across all six · Database access
- The model never sees your data
- It reads the schema; results return straight to the user. This is how to secure AI access to ERP data without a new access layer.
- Boundaries you define
- Roles and query limits set by the database owner, not the agent.
- On-prem capable
- Deployable on-premises or air-gapped where policy requires it. See our full AI development services.
Questions stuck behind your reporting team?
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The Result
Live answers, without handing over the data.
Because the agent works from the schema rather than the records, the vendor shipped a natural language ERP analytics tool to every customer without moving anyone's data. Decisions now come from live figures, not last week's export, landing roughly 35% faster with risk flagged up to 25% earlier.
50%
Less manual reporting effort, with answers drawn from live ERP data rather than a scheduled export. Decisions land 35% faster, with 100+ users now on one shared query engine.
How The Engagement Ran
Four phases, schema to rollout.
- Phase 1
- Discovery
- Reporting bottlenecks mapped with the vendor's team.
- Phase 2
- Schema layer
- Structure ingested without access to customer records.
- Phase 3
- Query agent
- SQL generation and validation tuned on real questions.
- Query agent
- Phase 4
- Rollout
- MCP boundaries set, then released across their customers.
- MCP
- LLM
- SQL generation
- ERP integration
- Role-based access
Why Neuramonks
Why the ERP vendor chose Neuramonks.
- Outcome-driven delivery
- Reporting-effort targets set before development started.
- Security-first architecture
- Schema-only access designed in from the first sprint.
- Built for multi-tenant
- One integration serving every customer of their ERP.
- Deployable on your terms
- On-premises or air-gapped where policy requires it.
Common Questions
What teams ask about this build.
What is natural language querying (NLQ)?
NLQ lets a person ask a database a question in plain English instead of writing SQL. This natural language ERP analytics tool reads only the schema and generates SQL from it, so results return to the user, not to the model.
How to secure AI access to ERP data?
Tie every query to the database's own structure rather than its records. The agent works from the schema, so the underlying data stays where it is and is never sent to the model.
What is the best natural language query tool for ERP?
The best fit is a tool built for your own ERP schema, including custom platforms, rather than a generic chatbot. The agent generates SQL for whatever structure it is given.
What did the engagement include?
The query agent, schema-only integration, the MCP tool layer, query validation, the self-service interface and multi-tenant rollout.
How is database access controlled?
Queries run inside boundaries the database owner defines, with roles and limits set by them rather than by the agent.
