Perplexity vs PropensityAI

Perplexity is an answer engine that searches the open web and returns answers with inline citations. It can search files and connected apps alongside the web, and can select among more than 20 leading models, including ChatGPT, Gemini, Claude, and Nemotron. Deep Research conducts searches across many sources and produces a report. Access is available on Android, iOS, macOS, web, and Windows, with an API also listed. The free plan has limited weekly use for simple questions. Pro costs $20 a month and adds Computer, premium data sources, web search, files, apps, model council, and model selection. Max costs $200 a month and offers higher limits for Deep Research, resource generation, file uploads, and monthly video creation. Enterprise Pro is $40 a month per seat; Enterprise Max is $325 a month per seat. Enterprise plans bill by seat, and API use requires separate credits. Perplexity lists more than 400 app connectors.

PropensityAI combines retrieval and reasoning to answer questions across web pages, documents, and media with concise inline citations. It links claims to sources, flags uncertainty, and can decline when evidence is insufficient. Image analysis includes OCR, captioning, visual question answering, and structured extraction. Audio tools transcribe, identify speakers, summarize, and extract tasks. Video search locates scenes and spoken content with time-range citations. Users can query research PDFs and citations, then compare methods, results, and limitations. The maker describes connectors for shared drives, wikis, ticketing systems, and internal documents. Starter costs nothing, needs no sign-up, and allows up to 16 sources and one file per query. Local packages cost €8,000 as a one-time purchase for 30B Science & Logic or 30B Software Engineering, and €20,000, paid once for 130B General Intelligence. Team and Enterprise plans add shared connectors, controls, analytics, on-premises or VPC deployment, governance, and support. Enterprise can run local systems inside customer infrastructure, including air-gapped environments. The site says private queries and uploads are not used to train foundation models.