DeepResearch maintains up-to-date performance by combining real-time data ingestion, continuous model retraining, and adaptive source validation. The system prioritizes fresh data while ensuring reliability through automated checks and user feedback. This approach balances speed with accuracy, even as sources evolve.
First, the platform uses a distributed web crawler that continuously scans and indexes updates from priority sources. Instead of full-site rescans, it employs incremental updates based on change detection (e.g., monitoring RSS feeds, API webhooks, or HTML checksums). For time-sensitive domains like news or stock prices, dedicated pipelines process updates within minutes using streaming data architectures. Machine learning models automatically weight newer information higher in results while maintaining context from historical data through temporal embeddings.
Second, DeepResearch implements version-aware parsing and schema adaptation. When websites change their structure, differential analysis of DOM elements triggers parser updates through a combination of automated XPath/Schema.org pattern matching and human verification via a crowdsourced contributor network. For APIs, the system monitors version deprecation headers and automatically tests fallback endpoints. A dedicated "source health" scoring system downgrades or temporarily excludes unstable sources while alerting maintainers, ensuring the overall index remains consistent even when individual sources break.
Finally, the system performs daily model retraining with automated drift detection. Anomalies in query patterns or result click-through rates trigger immediate partial retraining cycles. A shadow mode deployment pipeline tests updated models against archived queries before promotion, preventing regressions. For rapidly changing domains like COVID-19 research during the pandemic, DeepResearch implemented specialized monitoring that tracked preprint server updates and WHO guideline changes hourly, demonstrating its ability to scale update frequency when required while maintaining strict source credibility checks.