--- title: Just-in-Time Ontologies: What Cheap Intelligence Means... description: A pipeline stripped the minus signs from loan amounts and turned a net $784,000 into $5.3 million of debt, with multiple models agreeing on the wrong result.... canonical: https://daily.dev/posts/just-in-time-ontologies-what-cheap-intelligence-means-for-data-hrishi-olickel-southbridge-ai-bj6vhbk4y twitter:card: summary_large_image twitter:site: @dailydotdev og:type: website og:site_name: daily.dev og:title: Just-in-Time Ontologies: What Cheap Intelligence Means for Data — Hrishi Olickel, Southbridge AI | daily.dev og:description: A pipeline stripped the minus signs from loan amounts and turned a net $784,000 into $5.3 million of debt, with multiple models agreeing on the wrong result.... og:url: https://daily.dev/posts/just-in-time-ontologies-what-cheap-intelligence-means-for-data-hrishi-olickel-southbridge-ai-bj6vhbk4y og:image: https://api.daily.dev/og/posts/BJ6vhbk4y.png og:image:alt: Just-in-Time Ontologies: What Cheap Intelligence Means for Data — Hrishi Olickel, Southbridge AI og:image:width: 1200 og:image:height: 630 og:locale: en --- > ## Documentation Index > Fetch the complete documentation index at: https://daily.dev/llms.txt > Use this file to discover all available pages before exploring further. # Just-in-Time Ontologies: What Cheap Intelligence Means for Data — Hrishi Olickel, Southbridge AI **[AI Engineer](https://daily.dev/sources/aidotengineer)** · 21 min read · 0 upvotes · 0 comments ## Summary A pipeline stripped the minus signs from loan amounts and turned a net $784,000 into $5.3 million of debt, with multiple models agreeing on the wrong result. Hrishi Olickel uses failures like this to explain why transforming data requires more than a universal schema or a confident agent. A customer means something different to billing, support and compliance. Useful structure depends on the source, the storage constraints, the decision being made and the assumptions a model brings to the task. His alternative is a just in time ontology: build the transformation when the question is known, and preserve its context and evidence for reuse. An Ohio campaign finance entity resolution project provides the working example. Olickel describes separating models that design a pipeline, models that perform its individual jobs and models that optimize the resulting system. Definitions, held out cases and disagreement checks matter as much as model selection. Comparing different model families can concentrate review on their disagreements, but shared assumptions can still produce the same wrong answer. The optimization loop needs a stable execution surface, bounded budgets, reusable methods and a defined way to fail. Small pilots establish a baseline; independently checked work segments make revisions easier to inspect and revert. Only then can the pipeline trade among cost, speed and accuracy without losing the evidence behind its answers. Speaker info: - https://github.com/hrishioa - https://www.linkedin.com/in/hrishioa/ Timestamps: 00:00 - Cheap transformation and an Ohio data pipeline 1:44 - Why a universal schema breaks down 4:28 - Building an ontology for the decision 5:34 - Three kinds of data workload 8:30 - Designers, workers and optimizing models 10:34 - Entity resolution and definition checks 13:58 - Model priors and silent data errors 15:41 - Cross model checks and shared mistakes 16:42 - Four requirements for self optimization 18:14 - Bounded pilots and reusable checked segments ## Full article daily.dev links to this article rather than hosting it. 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