applied AI · pipelines
Make the AI Pipeline Resumable Before Making It Smarter
Reliability and cost controls matter more than another model call when research runs span unreliable systems.
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Opportunity Radar →An AI research pipeline touches systems with different failure modes: search engines throttle, pages disappear, parsers encounter malformed input, local models time out, and paid APIs impose both rate and cost limits.
Opportunity Radar treats expansion, search, scraping, classification, extraction, and synthesis as durable phases. Each phase records its output and the run’s last completed boundary. A retry continues from that boundary instead of repeating every successful request that came before it.
Model selection follows the same operational logic. Fast local models remove irrelevant material and extract structure. A stronger paid model receives a smaller, better-bounded synthesis problem. Daily and monthly budget checks run before that paid boundary, so exceeding a limit produces a visible failed run rather than a surprise invoice.
Model quality matters. But a slightly better answer from a pipeline that cannot explain, resume, or bound its work is usually the worse system.