Telborg

Telborg ingests climate data exclusively from verified government sources, international institutions (IPCC, UNFCCC, World Bank), and corporate sustainability reports, then normalizes heterogeneous data formats (CSV, JSON, XML, PDF reports) into a unified schema for downstream analysis. The system likely implements ETL pipelines with source validation and metadata tracking to ensure data provenance and regulatory compliance for climate research.

Telborg implements a semantic search layer over its normalized climate dataset, allowing natural language queries to retrieve relevant climate metrics, reports, and time-series data without requiring SQL or specific field knowledge. The system likely uses embedding-based retrieval (vector similarity search) combined with structured metadata indexing to match user intent to climate datasets, with fallback to keyword search for precise metric names.

When the same climate metric is reported by multiple institutions with different methodologies or values, Telborg implements a reconciliation engine that flags discrepancies, explains methodological differences, and surfaces the most authoritative source based on institutional hierarchy and data freshness. This likely uses heuristic scoring (weighting IPCC > national governments > corporate reports) combined with metadata comparison to resolve conflicts.

Telborg retrieves relevant climate datasets, reports, and supporting evidence in response to research questions, synthesizing findings across multiple institutional sources to provide comprehensive context. The system uses retrieval-augmented generation (RAG) patterns, combining semantic search over climate data with institutional report indexing to surface authoritative evidence without hallucination.

Telborg normalizes climate metrics reported in different units and methodologies into standard formats (e.g., all emissions to CO2-equivalent, all energy to MWh), enabling cross-dataset comparison and analysis. The system implements a unit conversion engine with climate-specific rules (GWP factors for different greenhouse gases, energy conversion factors) and tracks conversion metadata to preserve scientific accuracy.

Telborg enables analysis of climate metrics over time, detecting trends, anomalies, and inflection points in emissions, renewable energy adoption, temperature, and other indicators. The system implements time-series analysis algorithms (moving averages, regression, change-point detection) on institutional climate data, with visualization and statistical significance testing to support climate research and policy analysis.

Telborg implements a data quality assessment engine that evaluates institutional climate datasets on dimensions like completeness, consistency, timeliness, and methodological rigor, assigning quality scores and flags to guide researcher confidence. The system uses heuristic rules (e.g., flagging data >2 years old as potentially stale) combined with metadata analysis to identify data quality issues without requiring manual review.

Telborg enables tracking of climate policies and emissions reduction targets against actual institutional data, comparing pledged targets (NDCs, corporate net-zero commitments) to reported progress. The system maps policy targets to relevant climate metrics, retrieves actual data from institutions, and calculates progress toward targets with visualizations and gap analysis.