Core Concept

Recall AI is a SAAS product that transforms your company's messy cloud drive (Google Drive, Dropbox, OneDrive) into an intelligent, conversational knowledge base. Instead of relying on flawed keyword searches, teams can ask questions in natural language and receive instant, synthesized answers with direct links to the source documents. It turns your passive archive into an active, on-demand expert on your own company's knowledge.

Value Proposition

Stop searching, start asking. Recall AI eliminates wasted time hunting for documents and information. It empowers your team to find answers, uncover insights, and make better decisions by instantly accessing the full depth of your company's accumulated knowledge. It turns your biggest data liability—the digital landfill—into your most valuable asset.

How It Incorporates AI

Recall AI is built on a modern Retrieval-Augmented Generation (RAG) architecture:

• Connect & Index: It securely connects to a company's cloud storage via OAuth. An AI pipeline then processes each file (including OCR for scanned PDFs), chunks the text, and converts it into vector embeddings using a state-of-the-art language model.
• Vector Search: These embeddings are stored in a specialized vector database. When a user asks a question, the question is also converted into an embedding to find the most contextually relevant pieces of information from the entire document library.
• Synthesize & Cite: The relevant information is then fed into a powerful Large Language Model (LLM), which generates a concise, human-readable answer. Crucially, every answer includes citations and direct links to the source documents, ensuring trust and verifiability.

The First MVP

The MVP for Recall AI is a simple web application with a focus on a single, seamless workflow:

• One-Click Integration: Users sign up and connect their Google Drive account with a single click.
• Automatic Indexing: The system begins indexing the first 1,000 .pdf, .docx, and .txt files in their drive. A progress bar shows the status.
• Simple Chat Interface: The main UI is a single chat box. Users can immediately start asking questions about the content of their indexed documents.
• Sourced Answers: The AI's response will be a synthesized paragraph followed by clickable links to the Google Docs from which the information was derived.

Unique Selling Points

• Zero-Configuration: It works with your existing file structure. No need to migrate data or manually organize files.
• Trust Through Citation: Unlike black-box AIs, every piece of information is verifiable with a link to the source, solving the enterprise trust issue.
• Speed to Value: A company can go from a chaotic Google Drive to a fully queryable knowledge base in minutes, not months. It is designed for small teams and can be quickly built and deployed.

Concept

OmniPost is a simple, developer-first API for content syndication. It follows the "Resend, but for Content" model. Instead of manually copying, pasting, and reformatting blog posts for platforms like Medium, Dev.to, Hashnode, and LinkedIn, users make a single API call. OmniPost handles the authentication, content transformation, image uploads, and proper canonical linking, saving creators hours of tedious work and maximizing their content's reach.

MVP (Minimum Viable Product)

The first version of OmniPost will be a simple REST API with the following functionality:

• Users can securely connect their accounts for 3 initial target platforms (e.g., Dev.to, Hashnode, Medium) via OAuth.
• A single API endpoint /syndicate that accepts a source URL or Markdown content.
• The service will parse the source content, cross-post it to the user's connected accounts, and automatically set the canonical_url on each syndicated post to point back to the original source, protecting SEO.
• Basic parsing to handle standard Markdown, code blocks, and images.

How it Incorporates AI

While the MVP can launch with robust parsing rules, generative AI is the core of its future value and differentiation:

• Smart Transformation: The API will use an LLM to intelligently convert source HTML/Markdown into the specific format required by each platform's editor, ensuring code blocks, embeds, and images render perfectly.
• Content Adaptation: A premium feature will allow users to specify transformations. For example: POST /syndicate { source: "...", targets: [{ platform: "linkedin", style: "professional_summary" }] }. The AI will generate a concise, professional summary of the article suitable for a LinkedIn post, complete with a link to the original.
• Intelligent Tagging: AI will analyze the article's content and automatically suggest relevant, high-traffic tags for each platform to increase discoverability.

Unique Selling Points

• API-First: Designed to be integrated into existing developer workflows, such as CI/CD pipelines or CMS webhooks. Publish an article on your own blog, and it's automatically distributed everywhere.
• Focus on Long-Form Content: Unlike social media schedulers, OmniPost is purpose-built for the nuances of technical articles, including complex formatting and SEO attribution.
• Solves a Real, Tedious Problem: It automates a universally disliked task for content creators, providing immediate and tangible time savings. Its impact is to become the essential infrastructure layer for programmatic content distribution on the web.