Project Name
Ksolves Gives Network Analysts Real-Time Druid Insights From Plain English
A network operations organization running at carrier scale had a real problem hiding behind a sophisticated system: its entire real-time telemetry estate lived in Apache Druid, capable of sub-second query responses across billions of events, but none of its Network Analysts or NOC Managers could actually query it themselves. A performance anomaly would surface, the analyst would know exactly what to ask, and then the question would sit in a BI team ticket queue until the window for action had already closed. Ksolves closed that gap with an AI ML consulting engagement: a Natural Language to Druid Query Engine that turns plain English questions about network performance, anomalies, and utilization directly into precise, real-time Druid queries. BI team dependency for routine network queries dropped by 70%.
- Network Analysts Unable to Query Druid Without Technical Intermediation: Druid's native query interface demanded precise knowledge of schema, dimension names, and aggregation syntax, so every analytical question had to route through a technical team before anyone got an answer.
- BI Team Bottleneck Introducing Critical Delays in NOC Operations: The BI team was the only path between a business question and a Druid result, and as query volume grew, their capacity became the hard ceiling on how fast NOC operations could actually move.
- Real-Time Network Anomalies Missed Due to Query Turnaround Lag: Anomalies and utilization spikes are time-bound, and the wait between spotting one and getting a BI-mediated query result often meant the window for intervention had already closed.
- Complex Network Metrics Requiring Multi-Dimensional Druid Queries: Questions spanning latency trends, packet loss by segment, and cross-layer anomaly correlation needed multi-dimensional queries with time-series aggregations that were non-trivial to write correctly even for experienced technical users.
- No Self-Service Analytical Capability for NOC Operations Teams: NOC Managers had no direct way to query performance data themselves, which meant every data question pulled them out of the moment they actually needed the answer in.
- BI Team Capacity Consumed by Routine Query Translation Rather Than Strategic Analysis: A large share of BI capacity went into translating straightforward questions into Druid syntax, work that required skill but added no analytical value of its own.
Ksolves designed a Generative BI platform purpose-built for network telemetry, combining a large language model translation layer with a domain-specific schema mapper so every generated query is dimensionally correct and aligned to the client's live Druid schema before it ever runs. The AI ML consulting work here focused on making that translation layer trustworthy, not just functional.
- Natural Language to Druid Query Translation Engine: An LLM-powered engine interprets free-text network questions, trend analysis, anomaly detection, utilization metrics, cross-segment comparisons, and generates syntactically correct, schema-aligned Druid queries with no human technical authoring at any stage.
- Network Domain Schema Mapper: A domain-specific mapping layer binds natural language concepts like latency, jitter, packet loss, and throughput to their exact Druid columns, dimension names, and time granularity parameters, so every generated query targets the right fields.
- Analytical Intent Classifier: A classification layer reads the query type behind each question- performance trend, anomaly investigation, utilization threshold- and selects the right aggregation strategy and filter structure before the query is even generated.
- Pre-Execution Query Validation Layer: Every generated query gets validated against the live telemetry schema before submission, confirming dimensional accuracy and time-range validity so malformed queries never reach the Druid cluster.
- Real-Time BI and NOC Dashboard Integration: The NL query engine sits directly inside the organization's existing BI dashboards and NOC tooling, so analysts get real-time visualized answers without switching to a separate query console.
Technology Stack
| Category | Technology |
|---|---|
| AI / NLP | Large Language Model (NL-to-Query Engine) |
| Database | Apache Druid (Real-Time OLAP) |
| Architecture | NL Query Intent Classifier |
| Platform | Network Telemetry Schema Mapper |
| Integration | Real-Time BI Delivery Layer |
| Methodology | Network Domain Query Validation |
- 70% Reduction in BI Team Dependency: Network Analysts and NOC Managers now resolve 70% of routine performance queries through the NL engine on their own, freeing the BI team for strategic analytical work instead of query translation.
- Query Results in Seconds, Not Hours: What used to be an hours-long round trip from analyst question to BI authoring to Druid execution now returns sub-second results directly to the person who asked.
- Faster Anomaly Detection and Response: NOC Managers can now investigate and confirm performance anomalies immediately instead of waiting on query-backed confirmation, closing the gap between spotting an issue and acting on it.
- Complex Multi-Dimensional Queries Generated Accurately From Plain English: Time-series trends, segment-level utilization, and cross-layer anomaly correlation now generate correctly from a plain English question, with schema validation confirming accuracy before execution.
- BI Capacity Redirected to Strategic Work: With 70% of routine query translation automated, the BI team's freed capacity now goes toward predictive modeling and strategic reporting instead of repetitive query authoring.
“Our NOC analysts can now ask a question in plain English and get a real-time answer from Druid in seconds. We’ve cut our BI team’s query backlog by 70% and our analysts finally have the real-time visibility they’ve always needed.”
– BI Director, Telecommunications
Network analysts in this organization could see an anomaly forming but had no way to ask Druid about it directly; every question meant a ticket, a queue, and a wait long enough for the moment to pass. Ksolves deep learning consulting work replaced that with a Generative BI layer that takes plain-language questions straight from analysts, NOC operators, and BI directors and returns accurate, real-time Druid results within seconds, no SQL required.
BI team dependency for routine query authoring dropped by 70%, and insight-to-decision time compressed from hours to seconds. Because every generated query gets validated against live Druid schema metadata before it runs, the system also closes off the risk of a malformed query returning misleading results, a real governance concern in network operations where bad data can trigger unnecessary maintenance work.
The same NL-to-Druid pipeline is already positioned to extend into security event logs, device inventory, and capacity planning data without rebuilding the core engine, and proactive anomaly narration, the system explaining what happened rather than just showing the metric, is the natural next step on this foundation.
Ready to Give Your Network Analysts Real-Time Druid Answers Without a Query Expert in the Loop?
