Semantic discovery for large organizations

The right match in your mesh

PeopleMesh helps companies and large organizations discover the right colleagues, internal job positions, opportunities, communities, and projects through semantic search that understands context, not just keywords.

Docker image Get the code

What PeopleMesh does

One semantic discovery layer across your organization.

People and Expertise Discovery

Find colleagues by real capabilities, context, and intent instead of only job titles or rigid filters.

Internal Opportunities Matching

Surface internal job positions, roles, projects, and initiatives that align with skills, goals, and organizational needs.

Communities and Collaboration

Connect employees with relevant groups and communities to speed up collaboration and knowledge sharing.

Why PeopleMesh exists

Large organizations often rely on fragmented discovery across chat, spreadsheets, and disconnected systems.

The problem

Talent, opportunities, and expertise are distributed across teams and tools. Discovery becomes slow, noisy, and dependent on tribal knowledge.

The outcome

PeopleMesh reduces time-to-discover, improves internal mobility, and helps organizations make better staffing and collaboration decisions.

How PeopleMesh works

Natural language in, high-signal ranked matches out.

Model the mesh

People, opportunities, communities, projects, and initiatives are represented as connected nodes.

Embed meaning

Each node is transformed into vectors with contextual metadata to capture semantic intent.

Rank best matches

User queries are embedded in the same space, then ranked to return the most relevant results.

Built for companies and large organizations

Enterprise-ready by design, with governance and trust at the core.

Privacy and compliance posture

Consent controls, retention policies, and GDPR-aligned workflows are built into the product direction.

MCP for AI workflows

Expose PeopleMesh through a Model Context Protocol server so assistants and tools can run semantic discovery with standard, secure integration patterns.