18 sources, one pipeline, every morning
Every day before 7:00 AM AEST, the agriIQ pipeline fetches data from 18 authoritative sources, scores it across seven dimensions, and generates close to one hundred AI-written narratives covering national, commodity, regional, and sub-regional conditions. By the time professionals open their dashboards, the entire picture has been refreshed—no manual data entry, no spreadsheet lag, no room for transcription error.
The data sources
agriIQ draws from a deliberately diverse set of government and industry sources. The Reserve Bank of Australia provides cash rate and lending rate data. The Bureau of Meteorology and the SILO DataDrill network supply rainfall observations across all 57 SA4 statistical regions. The Australian Bureau of Statistics publishes producer price indices and merchandise trade figures via its SDMX API. ABARES contributes farm survey data on profitability and export volumes. The Department of Agriculture, Fisheries and Forestry is monitored for biosecurity alerts. Meat & Livestock Australia and wool exchanges provide daily livestock and fibre prices. NASA’s FIRMS satellite system detects active fire hotspots, and Open-Meteo supplies soil moisture readings and dam storage levels.
Additional sources include Queensland drought declarations from the Long Paddock, Western Australian weather stations from DPIRD, and enhanced ENSO phase data combining SOI, Nino3.4 sea surface temperatures, and the Indian Ocean Dipole index.
The daily sequence: ingest, score, narrate
The pipeline runs in three stages. First, the ingestion phase: each of the 18 sources has a dedicated TypeScript module that fetches raw data, parses the source-specific format (CSV, JSON, SDMX XML, or HTML), and normalises it into a consistent structure. Each ingester returns a dimension identifier, a score between 0 and 100, and a detail object containing the underlying metrics.
Second, the scoring phase. Raw data is converted to 0–100 scores using calibrated thresholds derived from historical ranges. Rainfall, for example, is scored relative to the long-term average for each region and season. Commodity prices are normalised against five-year rolling averages. The thresholds are periodically reviewed to ensure they remain meaningful as conditions evolve.
Third, narrative generation. Anthropic Claude interprets the day’s scores and writes plain-English summaries across four tiers: national overviews, individual commodity outlooks, state and territory summaries, and sub-regional (SA4) analyses. In total, approximately 98 narratives are generated each cycle, split into four batches for efficient processing.
Quality and reliability
Reliability is built into every layer. Where possible, agriIQ uses redundant data sources—SILO rainfall cross-references BOM observations, and the wool ingester falls back from one exchange feed to another if the primary source is unavailable. Each ingester includes error handling that logs failures without halting the rest of the pipeline. A daily health check endpoint monitors the status of every upstream dependency: database, authentication, AI services, payments, and email delivery.
The entire pipeline is orchestrated by scheduled jobs running in sequence. If an individual ingester fails—say, the ABS SDMX API is temporarily unavailable—the previous day’s score is retained and the failure is logged for investigation. This approach ensures the dashboard always shows data, even if a subset of sources experienced transient issues.
Why automation matters
Manual data collection and analysis introduces lag and human error. A research analyst might update a rainfall spreadsheet weekly or fortnightly; agriIQ does it daily. A commodity report might take a week to compile and distribute; agriIQ’s narratives are ready before breakfast. For time-sensitive decisions—a loan approval pending a conditions check, a portfolio review ahead of a credit committee meeting—the difference between daily and weekly data can be material.
Automation also enables consistency. Every region is scored using the same methodology, every commodity assessed against the same benchmarks. There is no variation introduced by different analysts applying different judgement calls. The result is a platform that lenders and advisers can rely on for standardised, comparable, and timely intelligence.