Personal AI

Personal AI uses custom enterprise pricing rather than published self-serve tiers: its pricing page lists Enterprise Use with multiple AI Persona licenses, pro-trained Personal AIs, custom model capacity, custom integrations, enterprise security, a commercial license, and a 99.95% uptime SLA, but no exact public monthly or annual price.

Personal AI positions itself as a distributed edge AI platform built around Small Language Models rather than a generic large-language-model assistant. Based on the website content, its core promise is to provide unified memory and context for self-improving AI experiences that are private, programmable, and precise. The product is described as a Small Language Model platform engineered for scaled experiences, which suggests it is aimed at organizations, developers, or advanced users who want AI systems that can operate with more controlled context, memory, and behavior than a broad general-purpose chatbot.

The most distinctive framing on the site is the emphasis on memory architecture. Personal AI describes a Memory Core built around five memory primitives: encoding, stabilizing, storing, retrieving, and updating. Its documentation says each AI persona has segregated data and memory, and that training can happen through bulk uploads or continuous interaction. Supported training sources named in the vendor documentation include PDFs, Word documents, website content, Google Drive, OneDrive, Gmail, and Outlook.

The website also provides several concrete operating claims. Its homepage states production benchmarks measured against traffic on Comcast's network, including 15 ms time to first token versus a 1,000 ms cloud LLM baseline, $0.02 per million tokens versus $0.80 for Gemma-27B, and sub-500 ms end-to-end voice latency versus a 1,500 ms OpenAI Realtime comparison. These are vendor-published benchmarks from Personal AI's website, not independently verified benchmarks in this record, and should be validated in a buyer's own workload before procurement.

Security information is more specific than the earlier scrape suggested. Personal AI's public security and product materials state that it is SOC 2 and HIPAA certified, GDPR compliant, uses TLS 1.2+ for transmitted data, uses AES256 encryption for stored data, performs annual third-party network and graybox application penetration tests, conducts quarterly audits across access control, risk, information security, IT infrastructure, and HR procedures, and maintains backups with a maximum 24-hour RTO and RPO retained for 30 days. Buyers should request current audit reports and certification scope because this record relies on vendor-stated security claims.

For a professional directory audience, Personal AI should be evaluated as an infrastructure-style personal or enterprise AI memory platform. Its language is more technical than many personal assistant products: terms such as Small Language Models, AI Memory Architecture, Edge AI, and Distributed AI Platform indicate that the company is emphasizing architecture, privacy, and customization. Buyers should expect to confirm implementation scope, data handling, available APIs, supported integrations, and custom pricing directly with the vendor before treating it as an out-of-the-box replacement for everyday tools like note capture, CRM enrichment, or meeting transcription.

The main value proposition is not simply that it can answer questions, but that it can support AI experiences grounded in unified memory and context. That makes it potentially useful for personalized communication, knowledge recall, AI identity systems, internal knowledge agents, or enterprise experiences where precision and privacy matter. At the same time, the public pricing page leaves important commercial questions unanswered, including exact dollar pricing, seat minimums, usage caps, deployment requirements, and how much setup is needed to build and maintain useful memory.