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All digests tagged Data Architecture
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Kepler's architecture ensures LLMs cite numbers but never write them. By integrating deterministic code, a citation registry from 30M SEC documents, and a post-processing filter, the platform guarantees verifiable, auditable financial data.
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The most valuable context in finance isn't in a database, but in the investor's head. AI should augment human judgment, not replace it, by handling the data grind.
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Building agent quality platforms is a complex systems challenge. Learn why simple spreadsheets fail and how to build the continuous improvement loop using advanced tracing and observability.
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Dashboards are dead. The future of enterprise tooling belongs to AI agents. Learn how to build systems that perform complex, cross-application workflows by designing for agents, not for human clicks.
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AI agents are 'blind,' not stupid. The fix is context engineering at the data layer, requiring federation and logical views to unify data from sources like Kafka and Iceberg.
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Building trustworthy AI agents requires more than just an answer. The product must be designed to show its work, providing full data provenance, SQL queries, and intermediate calculations for expert verification.
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AI agents are not the revolution; they are the catalyst. The real impact is accelerating foundational changes in data, systems, and connectivity, forcing standardization and interoperability.
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In order to get reliable, uninterrupted access to the public web data on the internet, the knowledge layer needs to get the information from specialized tools. This is where the web data infrastructure layer comes into play.
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Clay details its agentic stack (Claygent/Sculptor), emphasizing that scaling requires robust evaluation suites, a data lake foundation, and leveraging large context models to build self-iterating loops.
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The web is no longer just a source of data; we can look at it as a source of context for knowledge work using AI agents. This leads to Context-as-a-Service (CaaS).
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Off-the-shelf LLMs provide fluent but inaccurate advice—the 'fluent bluff.' The solution requires grounding models in verifiable State-Action-Outcome records, moving finance toward outcome-driven AI.
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Video AI systems lack memory; this talk details building a durable 'memory layer' by treating video as a spatial temporal volume. The solution uses a Context Graph to enable deep reasoning over vast corpora.
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