Pipeline Component Architecture

An automated research pipeline has five components, each independently scalable and independently testable. The collector pulls data from sources on schedule or in response to triggers. The normalizer converts raw data into your internal schema. The analyzer applies analytical frameworks — credibility scoring, contradiction detection, pattern matching — to the normalized data. The synthesizer combines analyzed data into intelligence findings. The deliverer routes findings to consumers in the appropriate format and cadence. Each component connects to the next through a defined interface. When a component fails, the pipeline degrades gracefully instead of collapsing entirely.

1. Collector Pulls data from the source registry on schedule. Handles connection failures with retry logic and exponential backoff. Writes raw data to a staging area with source metadata and collection timestamp. The collector knows nothing about analysis — it collects, timestamps, and stores.
2. Normalizer Reads from staging, applies schema mapping and entity resolution, and writes normalized data to the analysis queue. Each source type has its own normalization adapter. Adding a new source type means writing a new adapter, not modifying the normalizer.
3. Analyzer Reads normalized data and applies analytical operations: credibility scoring, contradiction detection against existing data, pattern matching against configured alert thresholds. Writes analyzed data with enrichment metadata — credibility tier, contradiction flags, pattern matches.
4. Synthesizer Reads analyzed data and produces intelligence findings using convergence, mosaic, or dialectical synthesis. Generates draft briefs using AI-assisted summarization with the appropriate prompt chain for each finding type.
5. Deliverer Routes synthesized findings to consumers. Applies urgency classification. Formats for each consumer profile. Delivers through the configured channel — email, Slack, dashboard, API. Tracks delivery confirmation and consumption.