Project Name
Natural Language to SQL Platform for a Telecom Operator in the Middle East and South Asia
For a mid-market telecom operator serving markets across the Middle East and South Asia, the ability to act on data quickly had become a critical competitive requirement. Business analysts responsible for tracking network performance, commercial KPIs, and operational metrics were unable to access data independently. Every question required a formal request to the technical SQL team, and responses typically took 24 to 48 hours to fulfil.
This dependency was not a reflection of the analyst’s capability. It was a structural problem. Business users lacked SQL skills, the data team was overwhelmed by routine requests, and decisions were consistently delayed while queries moved through a manual queue.
Partnering with Ksolves, an AI-First Company, the telecom operator implemented a Natural Language to SQL platform that allowed business analysts to ask questions in plain English and receive accurate, validated results in under 60 seconds. Using a fine-tuned large language model, schema-aware query generation, and an integrated business glossary, Ksolves delivered a self-serve analytics capability that transformed how the organisation accessed and acted on its data.
The challenges faced by the client are as follows:
- SQL Dependency Blocking Business Users: Every data question required a formal request queued for technical execution. Business analysts had no independent path to the data they needed, creating a structural bottleneck that slowed reporting and decision-making across the organisation.
- Reporting Delays Impacting Decisions: Ad hoc queries took between 24 and 48 hours to fulfil. By the time results reached business teams, the context for the original question had often changed, reducing the value of the data provided.
- Data Team Overwhelmed by Routine Queries: The majority of the SQL team's capacity was consumed by requests that could have been self-served with the right tooling. Strategic and complex analytical work was consistently deprioritised as a result.
- Inconsistent Query Results: The same business question, submitted by different analysts or at different times, could return varying results due to inconsistent query interpretations by different team members. This eroded confidence in the data and complicated reporting.
- Limited KPI Visibility: The inability to rapidly query operational data left business leaders without timely visibility into key performance indicators, reducing the organisation's ability to respond to commercial and network events in real time.
Ksolves, an AI-First Company, designed and delivered a Natural Language to SQL platform built around a fine-tuned large language model, schema-aware query logic, and a structured business glossary tailored to the client's telecom data environment.
- Natural Language Query Engine: Deployed an LLM-powered translation layer that converts plain English business questions into optimised SQL queries aligned to the client's specific data schema, allowing analysts to interact with complex data without writing a single line of code.
- Schema-Aware Query Generation: Fine-tuned the model on the client's data architecture to ensure generated queries reference the correct tables, relationships, and join logic. This eliminated the ambiguity that had previously caused inconsistent results across the team.
- Instant Results Visualisation: Integrated an auto-visualisation layer that formats query results as tables, charts, and trend lines, presenting data in a consumable format without requiring any additional analyst effort.
- Query Validation and Confidence Scoring: Implemented a validation step that assesses every generated query before execution and assigns a confidence score, giving analysts transparency into result reliability and reducing the risk of acting on inaccurate outputs.
- Business Glossary Integration: Built a telecom-specific business glossary that maps commercial and operational terminology to the underlying data fields, ensuring that questions phrased in business language are translated accurately into technical queries.
Technology Stack
| Layer | Technology |
|---|---|
| AI / ML | Natural Language to SQL (NL2SQL) Engine |
| AI / ML | Large Language Model (fine-tuned on client schema) |
| Database | Telecom Data Warehouse |
| Analytics | Auto-Visualisation Engine |
| Platform | Business Glossary Engine |
- Self-Serve Query Access for Business Analysts: Business analysts can now ask questions in plain English and receive results in under 60 seconds, removing the dependency on SQL specialists for routine data requests and enabling faster, more confident decision-making.
- Ad Hoc Query Time Reduced from 48 Hours to Under One Minute: Questions that previously required a 24 to 48-hour turnaround through the technical team are now resolved instantly through the NL2SQL platform, returning results within a single working minute.
- Data Team Capacity Redirected to Strategic Work: With the NL2SQL platform handling the bulk of routine ad hoc queries autonomously, the data team has been freed from repetitive request management and is able to focus capacity on higher-value analytical and strategic work.
- Consistent and Validated Query Results: All queries are processed through the same validated engine, eliminating the result inconsistencies that previously arose from varying manual interpretations of the same business question.
- Increased Query Volume from Business Teams: With self-service access in place, business users submit queries directly and more frequently, increasing the volume of data-driven decisions made across commercial, operational, and executive functions.
“Our commercial team used to wait two days for data answers, now available in 60 seconds. The platform has completely changed how our analysts work. Data is no longer a bottleneck; it is now a competitive advantage we can act on in real time.”
-Head of Analytics
What began as a bottleneck problem became a foundational shift in how the organisation interacts with its data.
Ksolves, an AI-First Company, helped the telecom operator move beyond SQL dependency, delayed reporting, and inconsistent query results to a validated, self-serve analytics environment powered by natural language. Business analysts can now access accurate data in plain English, and the data team has been repositioned from query execution to strategic analytics.
Built on a fine-tuned large language model, schema-aware query generation, and a telecom-specific business glossary, the platform delivers consistent, validated results in under 60 seconds without requiring any SQL knowledge from the end user.
As data volumes grow and the speed of commercial decision-making increases, the ability to access information directly becomes a competitive differentiator. Through AI and ML Consulting Services, Ksolves helps organisations design and implement intelligent data access platforms that democratise analytics, reduce operational bottlenecks, and build the foundation for scalable, data-driven operations.
With the NL2SQL platform in place, the client is positioned to extend self-serve analytics across additional data domains and integrate predictive capabilities as the next phase of its data transformation.
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