Round 1 Winners!
Proof of Usefulness Report

Topos

Analysis completed on 6/1/2026

+45
Proof of Usefulness Score
You're In Business

Topos aims to solve a genuine problem of fragmented personal context using an AI node and knowledge graph (Neo4j), making it conceptually strong and highly relevant to the current market. However, as a pre-launch project with an audience reach of only 4 users, it currently lacks verifiable traction, leading to a minimal score in alignment with the 0-100 baseline for pre-launch startups.

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Score Breakdown

Real World Utility+17.5
Audience Reach Impact+0.5
Technical Innovation+9.0
Evidence Of Traction+0.25
Market Timing Relevance+8.0
Functional Completeness+3.5
Subtotal+38.75
Usefulness Multiplierx1.15
Final Score+45

Project Details

Description
Topos is a personal AI node—software that represents you across your digital life. It pulls fragmented data from the apps and tools you already use into one user-controlled context layer for search, AI interaction, and permissioned sharing. We’re building for founders, operators, and AI power users who want persistent personal context without handing ownership to a single platform.
Audience Reach
4 users. We will launch end of June.
Target Users
TOPOS is for people whose context is valuable but fragmented. Founders, creators, researchers, students, and knowledge workers generate useful data every day across chats, notes, files, calendars, and AI tools, but they lack a system that represents them across that information landscape. TOPOS gives them a personal AI node: one place to collect their data, protect their context, and activate it through AI for memory, decision-making, filtering, and opportunity discovery. Early users are founders, engineers, and AI power users building workflows around personal data, memory, and AI-assisted coordination.
Technologies
Other, Neo4j
Traction Evidence
TOPOS is currently pre-launch, so we do not yet have public traction URLs. Our expected ICP includes people and communities that benefit from shared context and transparency, including education communities, families, work teams, investor cohorts, founder networks, and AI-native professionals. Early interest has come from developers, investors, business people, and product people through community presentations, meetups, Luma events, and hackathons. The consistent feedback is that fragmented personal and group context is an obvious pain point, and that people need a trusted system for collecting, protecting, and activating that context through AI.

Algorithm Insights

Market Position
Growing utility with room for optimization
User Engagement
Documented reach suggests active user community
Technical Stack
Modern tech stack aligned with sponsor technologies

Recommendations to Increase Usefulness Score

Document User Growth

Provide specific metrics on user acquisition and retention rates

Showcase Revenue Model

Detail sustainable monetization strategy and current revenue streams

Expand Evidence Base

Include testimonials, case studies, and third-party validation

Technical Roadmap

Share development milestones and feature completion timeline