Show HN: PeopleMesh, Semantic Search for People
PeopleMesh is an AI-powered semantic search platform designed to help organizations improve internal discovery and collaboration. It enables users to find colleagues, projects, communities, and opportunities using natural language queries, leveraging embeddings and metadata for relevant results. Built with privacy and security in mind, it supports GDPR compliance and offers open-source core software with enterprise extensions.
- ▪PeopleMesh uses semantic search and vector embeddings to match users with relevant colleagues, projects, and internal opportunities.
- ▪The platform is privacy-first, featuring granular consent controls, pseudonymized audit trails, and GDPR-aligned data rights workflows.
- ▪It operates as a graph-like mesh of organizational nodes and supports integration via web app, API, and MCP for AI assistants like ChatGPT and Claude.
- ▪PeopleMesh is open-source under Apache 2.0, with enterprise plugins available separately for systems like Slack, LinkedIn, and Workday.
- ▪The platform can be run locally using Docker, Java, and Maven, with development support for Ollama and OpenAI backends.
Hacker News (Newest) files mainly under programming. We currently carry 5,306 of its stories.
Story provenance
Source · retrieval · rights · ranking — open for full record
inspect →
Attribution is not the same as permission. This drawer separates discovery metadata, excerpts, WeSearch-generated summaries, reuse status, and whether the publisher receives the visit. Nothing here claims a legal grant the publisher has not made.
Record
| Original publisher | GitHub |
| Canonical URL | https://github.com/francescopace/peoplemesh |
| Publication time | Tue, 28 Apr 2026 20:57:14 +0000 |
| Retrieval time | 2026-04-28T21:14:39.821Z |
| Last seen | 2026-04-28T21:14:39.821Z |
| Headline source | Publisher (no WeSearch rewrite) |
| Excerpt source | publisher body |
| Excerpt method | First ~120 words (~800 chars) of extracted publisher body, fair-use limited. |
| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | FlnjQ-h5QbEM |
| Cluster logic | Grouped by semantic title/content similarity across sources within a rolling window. Same-publisher template collisions are excluded from coverage comparison. |
| Ranking reason | Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking. |
| Publisher visit | Yes — open original |
| Substitutes article? | No — link-out required for full text |
Rights status (four layers)
WeSearch handling by dimension
| Indexing | May the item be indexed (stored, ranked, made findable)? | Allowed |
| Snippet | May a short excerpt of the publisher's text be shown? | Allowed |
| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
Opening excerpt (first ~120 words) tap to expand
PeopleMesh The right match in your mesh. PeopleMesh is the AI-powered matching layer for modern organizations. It helps people discover the right colleagues, internal opportunities, communities, and projects through semantic search that understands context, not just keywords. By combining embeddings with metadata-based ranking, PeopleMesh surfaces high-signal matches faster, cuts through noise, and improves internal mobility and collaboration. Built privacy-first, PeopleMesh includes granular consent controls, configurable retention, and GDPR-aligned data rights workflows. Available via web app, API, and MCP integrations for AI assistants. Open-source at the core. Enterprise-ready in practice. Never built on personal data monetization.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at GitHub.