Blackwise

Location:

United States

Partnership period:

2022-2023

Industry:

Various

Technologies:

Laravel, PHP, Python, Elasticsearch, Google MAP, Application, Containers, DevOps, CICD, Data & Analytics, Site Reliability Services,

Team Size:

4 Frontend Developer, 6 Backend Developer, 4 Database Engineer, 2 DevOps Engineer

Competitive Environment

As an independent search engine startup, Blackwise sought to compete against Google's staggering 90%+ dominance of global search traffic. While viable alternatives existed like Microsoft Bing and DuckDuckGo, most replicated Google's ad-supported business model showed sponsored results frequently out of context ahead of organic listings. However, increasing public disenchantment around issues like misinformation, filter bubbles, and rising privacy concerns opened a market gap.

Blackwise envisioned disrupting the status quo with an AI-driven relevance engine optimizing search results strictly to user intent, rather than maximizing engagement and data collection. By selectively indexing sites favoring authority, accuracy, originality, and ethical information commerce over popularity, search aimed to showcase community-supported internet treasures while providing guardrails against content violating policies. Personalization features logging search histories and learning searcher interests through behaviors also sought to deliver hyper-relevant custom experiences aligned with shifting consumer preferences away from one-size-fits-all outcomes.

Problem Statement

Dominant search platforms increasingly compromised user trust and autonomy through opaque practices maximizing addiction and data extraction. Result relevance suffered from not matching unique user contexts. Business models depended on pervasive surveillance to microtarget behavioral advertising often against user well-being. Lacking alternatives balancing sustainability with quality content curation, privacy protections, and customization, consumers faced the dilemma of tolerating harm or missing out on access to knowledge.

Required a next-generation search platform respecting both community information quality standards and personalization preferences while sustaining organic traffic value. Commercial success necessitated carefully considered advertising units not betraying user agency or undermining content integrity promises. Advanced algorithms promised a path balancing ethical commerce and consumer advocacy.

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.

Phases

Phase 1

Phase 2

Phase 3

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.

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.

Blackwise

Blackwise

Competitive Environment

Problem Statement

Solution and Objectives

Technology Planning