RaLHF
Bot Food Corporation offers RaLHF (pronounced "Ralph"), a context assistant that collects a user's digital life—from subscriptions and travel history to watchlists and purchase data—and feeds it to their AI. The platform curates and prioritizes this personal context, enabling AI assistants to provide more personalized recommendations and answers without users having to repeatedly explain their background. RaLHF also addresses the "AI exhaust" problem in companies by creating a shared, organized context layer that lets AI tools across departments build on each other's work.
- Artificial Intelligence
- Enterprise Software
- AI Agents
- Software Only
Funding
Founders
Founder details are not available yet.
Product
Problem
AI assistants often lack the deep, personal context needed to provide truly useful and personalized responses. Users must repeatedly explain their background, preferences, and history, while the richest data—purchase history, travel patterns, entertainment tastes—remains locked inside walled-garden accounts that AI cannot access. In companies, AI-assisted work is scattered across private chat windows, preventing teams from building on each other's output and leading to duplicated efforts and contradictory plans.
Solution
RaLHF (pronounced "Ralph") is a context assistant that collects a user's digital life from the accounts and files they choose, keeps that context fresh, and feeds it to their AI. It gathers data from web apps, files, folders, and email, then curates and prioritizes the information so the AI understands what matters. For businesses, Bot Food is building a shared context layer that saves, organizes, and makes discoverable the "AI exhaust" produced across departments, so every AI in a company works from the same current, shared picture. This approach mirrors the advantage developers have with GitHub, where a shared, organized data store lets AI see the whole project and build on colleagues' work.
Target Audience
Primary users are individuals who want more personalized help from their AI assistants and want to own the context their AI relies on, as well as companies seeking to improve AI-assisted teamwork across marketing, finance, sales, legal, and operations departments.
Features
- Collects context from user-selected web apps, files, folders, and email accounts
- Curates and prioritizes digital memory to filter out contradictory or outdated information
- Provides a context mediator layer that enriches signals and reduces noise for AI models
- Addresses the "AI exhaust" problem by creating a shared, organized home for AI-generated work across company departments
- Enables AI to discover and build on existing work, reducing duplicated efforts and contradictions
- Supports a "context moat in reverse" architecture where user context stays on their hardware while AI intelligence comes from API calls to chosen models