nowfound

Alternatives

Products that do what Lenzy AI – Turn AI agent conversations into actionable insights does

Hey HN, I’m building Lenzy AI - probably the first product analytics platform for AI agents. From my research: Companies building AI agents have thousands or even millions of conversations. In these, users express what they need, use, love, or hate. Often long before they reach out to support (or churn). Some teams try to read chats manually, some build in-house pipelines to analyze them, others completely miss out on this data. The idea: Lenzy continuously analyzes conversations users have with your AI agents to: 1. Discover missing features (e.g. "Fetch info from a URL" mentioned 42 times…

  1. 1

    Chat with your analytics

    2023

  2. 2
    Agnost AI287

    Catch agent failures your evals miss

    13d ago · agnost.ai

  3. 3

    Build AI agents that speak

    2024

  4. 4

    Generate a ChatGPT for your website In 1 minute

    2023

  5. 5

    Create and edit interactive funnels by chatting with AI

    2025

  6. 6

    AI agents for your business. Automate, generate, create!

    2025

  7. 7

    Analytics for your voice AI agent

    2024

  8. 8
    Dialog226

    AI voice agents that can talk to your users

    2025

  9. 9

    ChatGPT with your data on your website, Discord & more

    2023

  10. 10

    Revolutionize LLMs chat platform with pay-as-you-go pricing

    2025

  11. 11
    Talklab139

    AI powered chat analytics for customer insight

    2023

  12. 12

    Chat, convert, charm - your AI powerhouse for digital sales

    2023

  13. 13
    OneBot112

    AI-based conversational commerce tool

    2021

  14. 14
    AQX103

    Build AI voice agents to automate sales & support

    2025

  15. 15

    Uncover the truth in your relationship with AI chat analysis

    2025

  16. 16CA

    Current AI chat assistants face a fundamental challenge: context management in long conversations. While current LLM apps use multiple separate conversations to bypass context limits, a truly human-like AI assistant should maintain a single, coherent conversation thread, making efficient context management critical. Although modern LLMs have longer contexts, they still suffer from the long-context problem (e.g. context rot problem) - reasoning ability decreases as context grows longer. Memory-based systems have been invented to alleviate the context rot problem, however, memory-based…

    Nov 2025

  17. 17BG

    Hi HN, My name is Othmane and I’ve been in the ML field (building and shipping models) for the last ~5years. Today, as many people out there, I come across new AI tools every week. However I was a bit surprised to see little to no mention of established AI vendors that existed before chatGPT and how most use cases are heavily biased toward content generation (text/image) or conversational AI (chatbots). I built a tool that helps you find the right AI solution/provider based on your use case. It uses a curated database of 100+ solutions from established vendors. It covers things…

    2023 · preview.steerlab.ai

  18. 18IB

    Excited to share a project I’ve been building for months! Would love to receive honest feedback :) My motivation: AI is clearly going to be the interface for data. But earlier attempts (text-to-SQL, etc.) fell short — they treated it like magic. The space has matured: teams now realize that AI + data needs structure, context, and rules. So I built a product to help teams deliver “chat with data” solutions fast with full control and observability (agent tracing, quality scores, etc) — am I wrong? The product allows you to connect any LLM to any data source with centralized context…

    Oct 2025 · github.com

  19. 19
    Stand14

    AI website chat that pulls you in when it matters

    Jul 2026 · stand.chat

  20. 20AP

    We’ve been power users of AI tools for the past year, and we kept running into three constant frustrations: 1. Too many subscriptions – Paying separately for OpenAI, Anthropic, Perplexity, and others quickly adds up. 2. Losing memory & context – Switching between models or platforms means you start over each time. 3. Privacy concerns – With most closed-source models, your data may be stored or used for training. That’s not acceptable for sensitive or professional use cases. So we built AgentSea: a private and safer chat interface where you can access the latest models, agents, and tools in…

    2025 · agentsea.com

  21. 21AA

    I built a tool to solve a problem I kept running into: I was making product decisions based on guessing instead of real users. I kept building stuff nobody wanted as I was usually wrong. So, I built HolyShift: AI agents that validate product ideas by talking to real people on Reddit, HN, X, and LinkedIn … then generate a detailed GTM and “Should we build this?” report. No synthetic data (ChatGPT). No predictions. Only real conversations from real people. What it does • Posts platform-native questions (where allowed) • Collects real reactions, objections, pricing signals • Clusters feedback…

    Nov 2025 · app.holyshift.ai

  22. 22IB

    Hi HN, I built ChatOne while working on a project and constantly switching between AI models like GPT-4 and newer ones like Claude 3.5. I kept wondering if I was missing out on better answers, so I created ChatOne to get responses from multiple models at once and compare them easily. -Teddy

    2024 · chatone.io

  23. 23
    Loqara12

    AI chat and voice agents that turn conversations into sales

    Jul 2026 · loqara.com

  24. 24

    Hundreds of customer conversations in hours

    Apr 2026 · insightfull.ai

Ranked by how close each launch is in meaning, then by votes. Refine with a description →