Vector search and reranking SaaS API

Hacker News

Hi, https://vecrank.com is an API for indexing data in a vector database and performing vector search with reranking at zero infrastructure cost. We spent a lot of time building a proper semantic search first for another startup. Now we are testing whether it can save time for the developers new to vector search and validating their startup ideas. As a software engineer you can save weeks of building a custom vector search solution with VecRank. It uses Postgres with pgvector, Gemini embeddings and 1.5 Flash for reranking under the hood. Also, it can be integrated in no-code websites (e.g. Shopify), automatically import the products and provide search analytics and metrics. Key Features: - AI-powered Search and Reranking: Enhance your application's search accuracy and contextual relevance. - Cross-language API Support: Integrate with ease using SDKs in JavaScript, TypeScript, Python, or cURL. - Zero Infrastructure: Focus on innovation while VecRank handles the heavy lifting. - Scalability: Designed to grow with your business, ensuring robust performance at all stages.

Visit product View on Hacker NewsJun 30, 2024

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Indie HackersFits the IH revenue-focused audience · Strong signals: gemini · Missing: supports, reddit linkedin, podcasting

93%93% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

Product HuntOn track for Day 1 leaderboard · Strong signals: new, context, gemini · Missing: mac, agents, macos

80%80% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released

54%54% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal

37%37% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

20%20% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Strong signals: saas, shopify · Missing: arr, mrr, revenue

18%18% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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