AddSearch
AddSearch transforms the way organizations connect users with information. More than just a traditional site search, AddSearch now offers AI Answers and AI Conversations, enabling businesses to deliver direct, conversational, and context-aware responses to user queries. These advanced capabilities complement AddSearch’s proven site search and content recommendation solutions, helping organizations create effortless, engaging, and personalized digital experiences.
With AddSearch, you can choose between AI-driven answers, conversational interfaces, or lightning-fast search results—all fully customizable for websites, e-commerce platforms, or web applications. Our Crawler and Indexing API ensure your content is always up-to-date, while our expert implementation services save valuable developer time and maximize results.
Today, nearly 2,000 customers worldwide—across Media, Telecommunications, Government, Education, E-commerce, and more—trust AddSearch to provide best-in-class search and AI-driven discovery.
AddSearch product portfolio includes:
- AI Answers – instant, accurate, and direct responses powered by generative AI.
- AI Conversations – natural, chat-like interactions for deeper user engagement.
- Autocomplete & Smart Ranking – predictive suggestions and optimized result ordering.
- Personalized Search – tailored experiences based on behavior and preferences.
- Content & Product Recommendations – boost engagement and conversions.
- Advanced Analytics – insights into user behavior
- Flexible Content Controls – include/exclude content, synonyms, filters, and facets, promote
- Enterprise Features – SSO, organizational user management, audit logs, SLA up to 99.999%.
- Seamless Implementation – works with any CMS, via crawler or API
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Planview Software Product Delivery
Planview Software Product Delivery Solution is a comprehensive enterprise platform that provides delivery intelligence by connecting strategy to execution across development toolchains. It integrates seamlessly with tools such as Azure DevOps, GitHub, and Jira to collect and unify real-time data from across teams. This allows organizations to gain full visibility into their delivery processes and make informed decisions. The platform includes features like cross-team dependency management, capacity planning, and agile planning at both team and portfolio levels. It enables users to analyze workflows, identify bottlenecks, and optimize delivery performance.
Advanced analytics, including DORA metrics, provide insights into engineering efficiency and outcomes. AI-powered roadmapping helps align business objectives with execution strategies. The solution also supports connected OKRs to ensure teams stay aligned with organizational goals. Portfolio-level investment planning and scenario modeling allow leaders to evaluate different strategies. Risk signals are surfaced early through configurable thresholds and flow metrics. By replacing manual reporting with real-time dashboards, Planview improves transparency and decision-making. Ultimately, it helps enterprises deliver digital products more efficiently and with measurable impact.
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LLM Council
The LLM Council serves as a streamlined orchestration tool that allows users to simultaneously query various large language models and consolidate their responses into a singular, more reliable answer. Rather than depending on a single AI, it sends a prompt to a group of models, each generating its own independent response, which are then evaluated and ranked anonymously by the others. Subsequently, a designated “Chairman” model synthesizes the most compelling insights into a cohesive final output, akin to a group of experts arriving at a consensus. Typically, it operates through a straightforward local web interface that features a Python backend and a React frontend, while also connecting to models from providers like OpenAI, Google, and Anthropic via aggregation services. This systematic peer-review approach aims to uncover potential blind spots, minimize hallucinations, and enhance the reliability of answers by incorporating diverse viewpoints and facilitating cross-model evaluation. With its collaborative framework, the LLM Council not only improves the quality of the output but also fosters a more nuanced understanding of the questions posed.
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DataGemma
DataGemma signifies a groundbreaking initiative by Google aimed at improving the precision and dependability of large language models when handling statistical information. Released as a collection of open models, DataGemma utilizes Google's Data Commons, a comprehensive source of publicly available statistical information, to root its outputs in actual data. This project introduces two cutting-edge methods: Retrieval Interleaved Generation (RIG) and Retrieval Augmented Generation (RAG). The RIG approach incorporates real-time data verification during the content generation phase to maintain factual integrity, while RAG focuses on acquiring pertinent information ahead of producing responses, thereby minimizing the risk of inaccuracies often referred to as AI hallucinations. Through these strategies, DataGemma aspires to offer users more reliable and factually accurate answers, representing a notable advancement in the effort to combat misinformation in AI-driven content. Ultimately, this initiative not only underscores Google's commitment to responsible AI but also enhances the overall user experience by fostering trust in the information provided.
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