Nyra — AI Insight Partner enabled a global enterprise to convert natural-language business questions into governed, explainable analytics without relying on SQL experts or BI teams. Fragmented data sources, rigid dashboards, and manual reporting bottlenecks slowed decision-making across finance, operations, HR, and sales. Nyra integrated seamlessly with enterprise systems to provide governed SQL generation, transparent reasoning, cited insights, and instant dashboards. As a result, decision cycles improved by 70%, analyst workload reduced by 60%, and leadership gained confidence through lineage-rich, audit-ready insights.
Customer: Confidential (Global Enterprise)
Industry: General / Multi-departmental (Finance, Sales, HR, Operations)
Location: Global
Company Size: 5,000+ employees
The customer faced significant delays in generating analytical insights due to siloed datasets, dependence on analysts, and rigid BI dashboards. Users lacked self-service capabilities, forcing every analytical request—simple or complex—through data teams. This created multi-day turnaround times, inconsistent reporting, and limited operational agility.
Existing BI tools were difficult for non-technical users to explore and modify. Fragmented data across Snowflake, Salesforce, Postgres, and ERP systems resulted in duplication and discrepancies. Leadership required governed, transparent insights to meet compliance and accuracy standards. Strict security and regulatory requirements demanded data lineage, access governance, and fully auditable transformations. The organization needed a unified, intelligent layer that could democratize analytics while enforcing governance and delivering enterprise reliability.
The enterprise operated legacy BI systems burdened by technical debt, inconsistent schema definitions, and complex access controls. Data was scattered across multiple warehouses and operational systems, making cross-domain analytics slow and error-prone.
Mapping high-level questions to multi-table, schema-accurate SQL was difficult. Row-level security, governance, and versioning were essential but not well supported by existing systems. Scalability issues surfaced due to large datasets and concurrency requirements across departments. The organization needed a platform capable of LLM reasoning, governed SQL execution, and scalable ingestion without compromising compliance or security.
Nyra was deployed as a multi-agent decision intelligence system supporting context-aware SQL generation, governed execution, and explainable analytics. It integrates with cloud data warehouses and enterprise knowledge systems to interpret questions, generate validated SQL, and deliver insights with tables, charts, and narrative summaries.
Nyra ensures every insight is grounded in real data, with citations, lineage trails, and reasoning transparency. Its multi-agent architecture supports conversational insight exploration, multi-step reasoning, KPI interpretation, and automated dashboard creation. The system unifies analytics across Snowflake, Databricks, MySQL, Postgres, Salesforce, and more—while enforcing enterprise governance.
Amazon Route 53: Global user routing and DNS resolution.
Network Load Balancer (NLB): High-performance traffic distribution into EKS.
Amazon EKS: Hosts all AI agents, orchestrators, and reasoning workflows.
Amazon Bedrock: Provides foundation models powering analytical reasoning.
Amazon S3: Stores logs, lineage files, metadata, and exported insights.
Amazon Aurora Postgres / Postgres: Governs operational metadata and insight storage.
Amazon ElastiCache: Accelerates agent-to-agent reasoning through caching.
Amazon Neptune: Stores graph-based context for schema reasoning and lineage.
Amazon CloudWatch (Logs, Metrics, Alarms): Monitoring, tracing, alerting, and operational health.
MySQL: Connected via MySQL MCP Server to access customer external operational systems.
SQL MCP Server: Enables secure, governed SQL access to customer-linked structured systems.
Postgres MCP Server: Provides contextual data access to external Postgres environments.
Databricks: Supports ETL, analytical workloads, and large-scale transformation pipeline.
The solution was implemented using an Agile methodology with iterative refinement cycles. Nyra’s multi-agent architecture was deployed on Amazon EKS using containerized microservices, allowing horizontal scaling and high availability. Schema metadata ingestion pipelines were created to synchronize table structures, relationships, and business definitions into Amazon Neptune.
Governed SQL generation and execution pipelines were developed using AWS Glue Catalog, ensuring lineage, transparency, and access control. CI/CD pipelines were built using GitHub Actions and EKS deployments to automate testing, versioning, and rollouts. Security controls included VPC isolation, IAM-based access segmentation, encryption, secrets rotation, and audit logging.
Integration connectors were configured for Snowflake, Databricks, MySQL, and Postgres MCP servers, enabling unified analytics. The final system enabled real-time conversational analytics, automatic dashboard generation, and cross-department collaboration. The complete implementation, including testing and user enablement, was completed in 10 weeks.
The solution followed AWS Well-Architected Framework principles with emphasis on performance, reliability, and security. Multi-agent orchestration improved reasoning accuracy while reducing LLM hallucinations. Knowledge-graph-driven context modeling ensured schema correctness and governed SQL generation.
CI/CD automation and containerized deployment allowed rapid iteration and minimal downtime. Advanced caching strategies with ElastiCache reduced inference latency. The project demonstrated innovative use of Bedrock models combined with MCP-based data access, enabling fully governed enterprise analytics.
Nyra significantly improved the customer’s operational efficiency by accelerating analytics delivery. Decision-making time improved by 70%, and dependency on analytics teams dropped by 60–65%. Teams gained immediate access to insights without manual querying or dashboard navigation.
The system reduced BI backlog, improved forecasting accuracy, enhanced financial visibility, and increased cross-department transparency. Executives benefited from real-time dashboards automatically generated via plain-language prompts. Overall ROI was achieved within the first year through productivity gains, reduced labor dependency, and elimination of duplicated reporting tools.
Nyra delivered a 30–50% reduction in query latency due to optimized SQL and improved execution plans. Scalability improved via Kubernetes autoscaling, supporting high concurrency across departments. Governance capabilities strengthened significantly through lineage, RLS enforcement, encryption, and compliance-friendly audit logs.
Development velocity increased through automated templates and modular agent workflows. Reliability reached enterprise-grade SLAs with fault tolerance, autoscaling, and resilient orchestration.
"Nyra transformed the way our teams work with data. What once required analysts and long wait times can now be accomplished instantly through a simple question. The transparency, governance, and accuracy Nyra brings to analytics is unmatched."
— Director of Business Intelligence, Global Enterprise
The team overcame significant challenges integrating heterogeneous data systems and ensuring SQL accuracy across multiple domains. Governance requirements necessitated strict logging, lineage, and role-based access enforcement. Performance challenges were addressed by optimizing EKS workloads, configuring ElastiCache, and refining SQL patterns.
Adjustments were made to the knowledge graph design and schema mapping approach based on iterative feedback. Continuous refinement improved reasoning accuracy and reduced false positives.
Key learnings included the importance of domain-driven schema modeling, automated SQL validation pipelines, and knowledge graph enrichment. Using AWS Well-Architected best practices ensured security, reliability, and scalability.
Multi-agent reasoning emerged as a core advantage, enabling modular troubleshooting and extensibility. Continuous feedback loops with business teams ensured alignment and high adoption.
Future phases will expand Nyra’s domain agents for supply chain, finance, and real-time alerting. The customer plans deeper ERP integration, automated anomaly detection, and proactive insight notifications. Additional AWS services such as QuickSight for embedded reporting may be integrated. The partnership will continue with ongoing优化, user education, and feature rollout.