# Dilog vs Featurely Dilog records 341 monthly visits with 0% month-over-month growth and Free Trial, Paid, Contact for Pricing pricing. Featurely records 934 monthly visits with -81.4% growth and Contact for Pricing pricing. | Metric | [Dilog](https://openaitools.app/tool/dilog) | [Featurely](https://openaitools.app/tool/featurely) | | :--- | :--- | :--- | | Website URL | https://dilog.ai | https://www.featurely.ai | | Categories | AI Research Tool, AI Marketing, AI Predictions | AI Predictions, AI Models, AI Marketing, AI Product Manager | | Pricing Model | Free Trial, Paid, Contact for Pricing | Contact for Pricing | | Monthly Visits | 341 | 934 | | MoM Growth Rate | 0% | -81.4% | | Primary Market | India | India | | Direct Visits | 122 | 661 | | Review Score | 4.1 | 4.2 | | Saved Count | 0 | 0 | | Supported Platforms | Website | Website | ## Dilog Dilog 是一个客户模拟平台，而且它从真人开始。它不做样本问卷，而是与你的真实客户进行 2～3 小时的深度访谈，再把这些对话转化为它所说的「匿名反思画像」（ARP）——按照这些具体个人的思维方式、情感反应与决策逻辑构建的数字版本。结果就是一个你的整个团队随时可以提问的私密面板，用来测试文案、营销活动、设计方案与产品创意，几分钟就能拿到答案，而不是等上几周。 针对「为什么不直接用通用大模型提问」，Dilog 的核心论点是保真度。原始 LLM 会回归统计均值：它是所有人的平均，因而不代表任何具体的人。Dilog 的画像是从数百小时的长访谈中构建的，因而保留了深度、矛盾、非理性的品牌忠诚，以及只有在对话中才会暴露的意外底线。Dilog 公布与真人直接回答的匹配度约为 86%，并每季度与面板背后的真人重新校准，因此这项资产会不断复利累积，而不像一次性研究那样到期失效。 价格覆盖个人到企业。个人方案为每月 50 澳元约 7 次对话（350 积分），Researcher 75 澳元，Pro 125 澳元；另有从 12 澳元起含 24 积分的按量付费选项，并提供 14 天免费试用。组织方案包含基于你自己客户访谈定制的面板——构建周期 6～12 周、单独议价——起步为 Team 每月 1,000 澳元（不含 GST）、Growth 3,000 澳元、Enterprise 5,000 澳元，均含不限人数团队成员与滚动三个月积分余额。 **Pricing & Plans:** 提供免费试用，并有订阅与按量付费两种模式。个人方案：Starter 每月 50 澳元（350 积分，约 7 次对话）、Researcher 75 澳元、Pro 125 澳元。按量付费 12 澳元起含 24 积分。组织方案起步为每月 1,000 澳元（不含 GST，Team，15,000 积分）、Growth 3,000 澳元、Enterprise 5,000 澳元，定制面板的搭建费用单独议价。 - **基于访谈的画像:** 面板由与真实客户的 2～3 小时访谈构建而成，因此每个回答都能追溯到真人的原话，而非统计平均值。 - **7×24 随取随用的洞察:** 可以在早上 9 点开董事会前、前一晚 11 点提问。无需等待招募、排期或激励发放。 - **给出决策动因，而非只有答案:** 不仅看到客户选哪个，还能看到背后的动机、犹豫与决策触发点。 - **季度重新校准:** 每季度都会重新访谈面板背后的真人，画像因此持续演化，而不像一次性研究那样过期作废。 - **默认隐私与安全:** 默认匿名化，交互仅限本组织可见，提交的问题永不用于训练模型。 **Strengths:** 回答可追溯到真实访谈证据，而不是合成推测; 彻底省去招募与排期环节，决策前一夜也能提问; 能捕捉选择背后的原因，包括非理性忠诚与意外底线; 面板每季度重新访谈而持续改进，而非一次性研究后失效 **Limitations:** 组织级面板需要 6～12 周的构建周期才能上线; 价格以澳元计价，定位偏向常年做调研的团队; 个人通用面板的语境针对性弱于企业定制面板 [Profile (Markdown)](https://openaitools.app/tool/dilog.md) ## Featurely Featurely targets the decisions that cannot be quietly reversed: a drug launch price, a new vehicle trim, a financial product change. Instead of relying on a handful of hand-built scenarios, it constructs a simulated version of the market from aggregate data and mirrors the people who actually decide — prescribers, payers, patients, buyers — as synthetic stakeholders in the right proportions. Teams then run each response option against every scenario worth testing, many times over, and see which option wins where. A distinguishing feature is the causal explanation layer. Every simulated shift is traced back to a named mechanism such as switching cost, price anchoring or outcomes beating price, so the output is inspectable reasoning rather than an opaque score. The vendor publishes its prediction record — thousands of blind predictions, most directionally correct, with misses included — and offers security documentation including SOC 2 Type I attestation, a DPA and US/EU data residency. Engagements start with a scoping call and produce a first readout in about two weeks. The company says it works with Fortune 500 firms in pharma, financial services and automotive, whose names and data stay private. It is best suited to strategy, pricing and launch teams in regulated industries rather than to casual market research. **Pricing & Plans:** No public price list; pricing is consultative. Engagements begin with a scoping call and are commissioned per study, with a first readout typically delivered in about two weeks. - **Synthetic market mirroring:** Builds simulated stakeholders for every segment — buyers, payers, influencers — in the correct proportions from aggregate data. - **Hundreds of scenario runs:** Tests each candidate response against every relevant scenario repeatedly to show which option wins where. - **Causal explanations:** Traces each simulated shift to a named mechanism such as switching cost or price anchoring, making results auditable. - **Public prediction record:** Publishes thousands of logged blind predictions with accuracy statistics, including the misses. - **Enterprise security posture:** SOC 2 Type I attestation, DPA, US/EU data residency and no use of client data to train shared models. **Strengths:** Designed specifically for high-stakes, hard-to-reverse decisions; Explains why segments move, not just what the model predicts; Publishes a verifiable prediction track record including misses; Enterprise-grade security documentation and data residency options **Limitations:** No transparent public pricing; requires a sales/scoping conversation; Typical turnaround is around two weeks rather than instant self-serve; Oriented to large regulated enterprises rather than small teams [Profile (Markdown)](https://openaitools.app/tool/featurely.md) ## Frequently Asked Questions ### How do Dilog and Featurely compare in monthly traffic? Dilog has 341 monthly visits, while Featurely has 934. Traffic estimates describe audience size, not product quality. ### How do their growth rates compare? Dilog has a month-over-month growth rate of 0%; Featurely has -81.4%. ### How does pricing compare? Dilog: Free Trial, Paid, Contact for Pricing. 提供免费试用，并有订阅与按量付费两种模式。个人方案：Starter 每月 50 澳元（350 积分，约 7 次对话）、Researcher 75 澳元、Pro 125 澳元。按量付费 12 澳元起含 24 积分。组织方案起步为每月 1,000 澳元（不含 GST，Team，15,000 积分）、Growth 3,000 澳元、Enterprise 5,000 澳元，定制面板的搭建费用单独议价。 Featurely: Contact for Pricing. No public price list; pricing is consultative. Engagements begin with a scoping call and are commissioned per study, with a first readout typically delivered in about two weeks. ## Alternatives | # | Tool Name | Monthly Visits | MoM Growth Rate | Pricing Model | Summary | Profile (Markdown) | | :--- | :--- | :--- | :--- | :--- | :--- | :--- | | 1 | [Branded](https://openaitools.app/tool/gobranded-com) | 3,704,728 | +6.6% | Contact for Pricing | Targeted research audiences with AI-verified data quality | [.md](https://openaitools.app/tool/gobranded-com.md) | | 2 | [Listen Labs](https://openaitools.app/tool/listen-labs) | 1,254,632 | +22.1% | Contact for Pricing | AI-moderated customer interviews that turn conversations into decisions | [.md](https://openaitools.app/tool/listen-labs.md) | | 3 | [Outset.ai](https://openaitools.app/tool/outset-ai) | 518,287 | +18.4% | Contact for Pricing | Qualitative research depth at survey scale, moderated by AI | [.md](https://openaitools.app/tool/outset-ai.md) | | 4 | [IndexBox](https://openaitools.app/tool/indexbox-io) | 330,438 | +14.5% | Free Trial, Paid | AI-driven market intelligence and trade data for 10,000+ products | [.md](https://openaitools.app/tool/indexbox-io.md) | | 5 | [Productboard](https://openaitools.app/tool/productboard) | 297,500 | -9.3% | Freemium, Free Trial, Paid, Contact for Pricing | Agentic product management system for customer-driven roadmaps | [.md](https://openaitools.app/tool/productboard.md) | | 6 | [Future AGI](https://openaitools.app/tool/future-agi) | 121,741 | -7.8% | Freemium, Usage-based | Build, simulate, evaluate and monitor self-improving AI agents | [.md](https://openaitools.app/tool/future-agi.md) | | 7 | [Koji](https://openaitools.app/tool/koji-ai-native-research-platform) | 61,640 | +13.9% | Paid, Contact for Pricing | AI-led customer interviews and research insights at scale | [.md](https://openaitools.app/tool/koji-ai-native-research-platform.md) | | 8 | [Delve AI](https://openaitools.app/tool/delve) | 49,025 | -4.6% | Freemium | Generate data-backed personas, digital twins and synthetic research on demand | [.md](https://openaitools.app/tool/delve.md) | | 9 | [FutureSearch](https://openaitools.app/tool/futuresearch) | 34,474 | -32.7% | Freemium | Calibrated forecasts with the evidence trail attached | [.md](https://openaitools.app/tool/futuresearch.md) | | 10 | [Voicepanel](https://openaitools.app/tool/voicepanel) | 27,176 | -12.6% | Contact for Pricing | Customer and user research at scale, powered by AI conversations | [.md](https://openaitools.app/tool/voicepanel.md) | --- Source: https://openaitools.app/compare/dilog-vs-featurely More Markdown: [Categories](https://openaitools.app/categories.md) · [Traffic rankings](https://openaitools.app/rankings.md) · [llms.txt](https://openaitools.app/llms.txt)