TelcoDR

Using PostgreSQL Foreign Data Wrapper (FDW), we enabled SQL queries against Salesforce and other external systems. The FDW connects through system APIs (using securely managed credentials for each data source), presenting external data as queryable PostgreSQL tables within the platform.

This approach provides a unified query interface across disparate systems. Telcos can run the platform on-premise using Docker with AWS IoT connectivity, processing data locally while leveraging cloud AI capabilities for mapping intelligence.

We integrated AWS Bedrock using Claude 4.5 Sonnet for intelligent schema interpretation and mapping suggestions. The platform architecture supports Anthropic's latest models as they become available, ensuring access to state-of-the-art capabilities as the model family evolves. The AWS infrastructure provides enterprise compliance through built-in security controls, encryption, and audit logging.

The AI analyzes database schemas, identifies relationships between tables, and proposes mappings to TMF standards based on semantic understanding of telecommunications concepts and naming patterns.

Early versions relied on LLM agents to generate and validate mappings. Quality was inconsistent. We rebuilt the system using Langfuse evaluation pipelines with deterministic checks.

Now, agents propose mappings, but deterministic rules validate them against TMF specifications, data type compatibility, and relationship constraints. This hybrid approach improved mapping accuracy by 60% and made the system debuggable and auditable, critical for regulated telco environments.

The biggest technical lesson: LLM agents are powerful but insufficient for enterprise accuracy.

Pure agent-based approaches let the model generate and validate its own work. This creates inconsistency, hallucinations, and errors that compound across 1,300+ tables. When an agent maps a field incorrectly, downstream relationships inherit the error.

Deterministic evaluation separates generation from validation. We apply AI where it provides clear value: semantic understanding of legacy schemas and intelligent mapping suggestions. The deterministic validation layer then ensures accuracy.

• TMF specification rules (required fields, data types, cardinality)
• Database constraints (foreign keys, unique indexes, null handling)
• Relationship integrity (parent-child consistency, circular reference detection)
• Business logic rules (telco-specific validation from domain experts)

The platform runs on AWS using infrastructure as code (AWS CDK) with full CI/CD automation. ECS Fargate handles container orchestration with auto-scaling based on mapping workload. Aurora PostgreSQL provides the managed database layer with tenant-column-based multi-tenancy for data isolation required for telecommunications compliance.

For customers requiring data sovereignty, we deploy a lightweight Docker runtime on-premise. It connects to AWS IoT Core for secure communication with the cloud control plane. The on-premise runtime executes queries via FDW, sends schema metadata to cloud AI services for mapping suggestions, and applies results locally.

Beyond data mapping, the platform enables telcos to build modern BSS applications on top of legacy systems without migration risk.

Once legacy data maps to TMF standards, developers can build new applications against the standard API. The platform handles translation between TMF and the underlying Salesforce or SAP schema in real time.

Telcos get modern capabilities without replacing systems that represent decades of business logic. The legacy infrastructure stays in place. New applications interact through standardized TMF interfaces.

• Customer churn prediction using unified data across legacy systems
• Automated billing reconciliation across multiple source systems
• Real-time service provisioning with legacy system integration
• Compliance reporting that aggregates data from incompatible schemas