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A search work like Google for Black-owned businesses where the advertisers submit their sites and the AI algorithm ranks them according to their quality and user preferences.

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Solution and Objectives

Develop an AI-powered search and discovery platform focused on optimizing results relevance through machine learning while avoiding invasive data collection. Proprietary web crawlers would index sites favoring established domains with reputations for publishing thoughtful authoritative, accurate, original analyses over outrage-inducing partisan gossip or spam sites. Quality raters would leverage NLP assessing sentiment, comment sections, and references to flag potential misinformation. Browser diagnostics auditing web vital signs like page speed, security protocols, and accessibility compliance would capture user experience integrity.

Continually learn searcher intent through behaviors like keywords used, content interactions, and voluntary explicit feedback to serve hyper-relevant custom experiences. Build a staffed appeals process allowing sites initially rejected to suggest improvements or dispute decisions. Carefully designed opt-in advertising units could sell impression space similar to Google Ads while upholding strict content policies as direct castigation of the entire model is shortsighted. Provide superior search experience aligned to shifting consumer preferences valuing quality, customization, and privacy over solely maximizing traffic, stickiness, and revenue extraction. Attract niche of conscious users appreciating benefits uniquely enabled as an independent platform not beholden to short-term shareholders.

Technology Planning

Built on scalable Kubernetes cloud infrastructure leveraging Python, Elasticsearch, and React to balance cost efficiency with the ability to handle unpredictable viral spike scenarios. Leveraged distributed data stores for efficient crawling, indexing, querying, and relevance ranking at scale. Custom web crawler and curation algorithms vetted submissions extracting features for quality ratings while personalized recommendation algorithms applied deep learning against growing multivariate usage data over time. Advertising and commerce initiatives mandated policy protections preventing conflicts of interest undermining index integrity and user trust. Focus on sustainable architecture patterns and modularity to facilitate maintaining development velocity across expanding surface area capabilities.


Conclusion and Results

Over 125,000 vetted sites indexed until present covering popular niches enabling search success metrics comparable to early Google stages. Thoughtful curation balancing long-tail queries and head search terms while machine learning algorithms personalized results to individual user need over time, distinguishing from static incumbent search experiences. Allowing sites to fix issues and suggest improvements during indexing requests strengthened ecosystem relationships with content creators by upholding value co-creation principles. Contextual ad units sold placement opportunities similar to Google Ads but followed strict content policies verified by staff, pioneering alternative balancing sustainability and consumer advocacy.

Ongoing development continues work improving algorithmic capabilities and community trust through transparency while expanding offerings matching growing variances in searcher use cases. Nimble independent status fuels prioritizing aspects like quality and privacy that mainstream profit-driven platforms struggle to accommodate. As legislation continues trending augmenting consumer protections, Blackwise positioned itself as ready to address underserved market gaps.

Emotcia: The solution

Key Challenge

Scalability challenges existed assessing exponential submission growth while preventing indexed site TOS violations using only proprietary self-learning ranking algorithms rather than total automation or manual reviews. Similarly, citizen participation pipelines needed governance preventing malicious actors from poisoning index integrity. Resource-intensive neural ranking interpretations of search intent strived to balance precision-recall tradeoffs with real-time response times as query volumes grew. Policy decisions around advertising units and data practices required extensive debate upholding vision.

Next Steps

Foster citizen participation by expanding grammar tools identifying site issues and editing index metadata like titles and descriptions in a responsible crowdsourced manner. Use index breadth to develop buyer guides, product comparisons, and research dossiers on popular topics. Explore secure decentralized identity partnership integrating emerging permission-based data wallet transaction models enabling privacy-preserving personalization.

Emotcia: The solution

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