Running Oynix as a service, AI Agents

Oynix Agency Implementation, AI-Assisted Development Retainers

Learn how to build profitable AI-assisted development retainers by indexing client code repositories and team tools into Oynix's knowledge graph, then serving context to AI agents via MCP without token consumption. This course covers client onboarding workflows, knowledge graph configuration across 18 connectors, MCP setup for Claude and Cursor, and pricing strategies that pass token savings to your margin.

Open the decision record for Oynix

What does running Oynix for clients commit you to?

Published figures for this service. Blank fields are not published.

Monthly tool cost
Vendor cost basis: freemium. A free local tier is published for running entirely on a developer machine; paid team-sharing pricing is seat-based by the vendor but no dollar amount is published in pricing_tiers. Any dollar figure for the agency engagement is Not modeled, supply your labor rate and overhead.
Time to first value
Hours to value per the published time_to_value field; setup_complexity is medium
Payback
Not modeled
Guided implementation
8 hours

Is Oynix worth running as a client service?

The evidence supports Oynix as an internal engineering-memory service for agencies with real development workflows, with zero-token MCP retrieval via Claude Code, Cursor, and similar clients as the clearest differentiator and a free local tier as a low-risk validation path. What remains unknown is the paid vendor cost per seat and any client price, so agency margin, ROI timeline, and scalability across accounts cannot be modeled from this data.

An agency-fit judgement for reselling this service. It is separate from the tool description on the decision record.

Before you start

What has to be in place before the first client engagement.

Tools and subscriptions

  • GitHub account (required for authentication and repository sync per inputs)
  • Code repositories on GitHub, GitLab, Bitbucket, or local (required per inputs)
  • MCP-compatible AI client such as Claude Code, Cursor, or Gemini CLI (per key_integrations)
  • Optional team tool connectors: Jira, Confluence, Slack, Notion (per key_integrations)
  • Shared server for team collaboration, or local install on a developer machine (per pricing_page)

People and inputs

  • Ability to configure an MCP server (api_signals confirms Oynix runs as an MCP server)
  • Internal repo to validate the knowledge graph before client work (per core_functions)
  • Technical staff comfortable with connector mapping at medium setup complexity (per setup_complexity)
  • Process owner for the self-writing wiki and Ask Oynix answer review (per platform_features)

Included with the course

7 working documents for delivering this service.

  • Client Onboarding Checklist for Oynix Knowledge Graph Setupchecklist
  • MCP Configuration Template for Claude Code and Cursor Integrationtemplate
  • 18-Connector Sync SOP: GitHub, Jira, Confluence, Slack Setupsop
  • AI-Assisted Development Retainer Pricing Worksheetworksheet
  • Decision Wiki Auto-Generation Workflow Guideguide
  • Knowledge Graph Maintenance and Refresh Schedule Templatetemplate
  • Client Handoff: Ask Oynix Query Training and Documentationguide

Listed by name. These documents are not yet published as individual downloads.