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Oynix

Oynix is an engineering memory layer that indexes code repositories and team collaboration tools (GitHub, Jira, Confluence, Slack, and 14 others) into a searchable knowledge graph.

Oynix is an engineering memory layer, integrating with GitHub, GitLab, Bitbucket, and Slack. InnovaAI scores it 5.2/10 for agency resale.

Consider5.2/10

Agency Audit

Oynix builds a searchable knowledge graph from GitHub, Jira, Confluence, and 15 other engineering tools, then surfaces that context to AI agents via MCP without token consumption. It's purpose-built for software development agencies, engineering teams, and AI agent development shops that need to preserve institutional knowledge across distributed codebases and decision logs. The local-first architecture and zero-token retrieval make it a strong fit for agencies billing clients on retainer for AI-assisted development work. However, pricing is enterprise-only and seat-based, so resale economics depend on your client's team size and whether you can justify the seat cost against the productivity gain.

ConsiderNo WLEnterprise
Fit

5.2/10

Typical Margin

Depends on volume

Time-to-Value

2d 1-2 days

Complexity
Moderate
Consider
Fit52
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Best For
  • Your clients are software development shops or AI agent development teams that already use GitHub, Jira, and Confluence and need to reduce context-switching for AI-assisted coding.
  • You want to offer a retainer service that reduces onboarding time for new developers by automating institutional knowledge retrieval from existing repositories and decision logs.
  • Your clients run multiple AI agents (Claude Code, Cursor, Gemini CLI, or custom MCP servers) and need a unified memory layer to avoid redundant token consumption across agent calls.
Not For
  • Your client base is non-technical (marketing agencies, design shops, e-commerce) where code repositories and engineering tools are not in use.
  • You need transparent, predictable per-seat pricing to calculate client retainer margins; Oynix requires enterprise sales conversations with no published rate card.
  • Your clients use Slack or Microsoft Teams as their primary knowledge repository and lack structured GitHub/Jira/Confluence workflows to index.

Profit Path

Your Cost

Contact for quote

Market Range

$600–$1.5K/project

Revenue Model

Setup Fee

From 242 published agency rates in USA, 25th to 75th percentile x 20h of assumed delivery time. Rates are self-reported directory profiles, not observed transactions.

Platform Features

Core capabilities of Oynix

18-source knowledge graph sync

Oynix connects to GitHub, GitLab, Bitbucket, Jira, Linear, Confluence, Slack, Microsoft Teams, Notion, Google Docs, and 8 additional platforms to build a unified searchable index. For agencies, this means a single source of truth for client code, decisions, and team context without manual documentation.

Zero-token MCP retrieval for AI agents

Oynix operates as an MCP server, delivering relevant engineering context to Claude Code, Cursor, Gemini CLI, and other AI agents without consuming tokens. Agencies can offer faster, cheaper AI-assisted development retainers because context retrieval happens locally instead of via API calls.

Self-writing wiki from team decisions

Oynix automatically captures decisions, code changes, and session outputs into a searchable wiki. Agencies can reduce documentation overhead for clients by letting the tool extract institutional knowledge from existing GitHub commits, Jira tickets, and Confluence pages.

Source-attributed Ask feature

The Ask Oynix function returns answers with links back to the original source (GitHub commit, Jira ticket, Confluence page). This transparency helps agencies justify recommendations to clients and speeds up knowledge verification during code reviews or architecture discussions.

Local or shared server deployment

Oynix runs on a developer's machine for free or on a shared server for team collaboration. Agencies can choose between per-developer licensing (local) or team-wide retainers (shared server) depending on client size and budget.

Session output capture and re-indexing

Oynix captures outputs from AI agent sessions and feeds them back into the knowledge graph for future retrieval. This creates a compounding memory effect where each AI-assisted task makes the next task faster and cheaper.

What Makes Oynix Different

Unique advantages vs similar tools in this niche

Zero-token retrieval for AI agents

vs Traditional RAG systems that charge per token or require model calls

Oynix runs as an MCP server, returning graph facts without any model call on their side, so there's no token billing.

Seat-based pricing, not per-repo or per-file

vs Tools that scale cost with codebase size

A four million line monorepo costs exactly what a side project costs; only the number of people changes the bill.

Runs locally with BYOK and BYODB

vs Cloud-hosted knowledge tools that hold your data

Oynix ships the engine, not the vault; your data lives in your database on your infrastructure.

Value Equation

Outcome-likelihood-time-effort assessment for Oynix

Value math requires real pricing

The Value Equation (dream outcome × likelihood ÷ time × effort) feeds directly into ROI math. Oynix has no published pricing, so we hold this section until real numbers are available.

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Pricing

Platform cost for Oynix

Custom pricing

Oynix uses custom/enterprise pricing: rates aren't published publicly. Contact their team directly for a quote.

Contact Oynix

Market Intelligence

Offer + scale economics for Oynix

Offer economics require real pricing

Offer economics, scale projections, and margin potential all depend on Oynix's actual platform cost. Once pricing is published or shared with your agency, we'll compute the full breakdown here.

Contact Oynix

Investment Decision Framework

Strategic vetting analysis for Oynix

Vetting Verdict

Consider

Favorable fit, worth a closer look

Agency Fit(white-label + resell pathway)
52/100
0255075100
Resell Friction(WL + mode + complexity)
60/100
0255075100

Buy If

4
STRATEGIC DRIVER

You want to offer a retainer service that reduces onboarding time for new developers by automating institutional knowledge retrieval from existing repositories and decision logs.

OPERATIONAL FIT

Your clients are software development shops or AI agent development teams that already use GitHub, Jira, and Confluence and need to reduce context-switching for AI-assisted coding.

OPERATIONAL FIT

Your clients run multiple AI agents (Claude Code, Cursor, Gemini CLI, or custom MCP servers) and need a unified memory layer to avoid redundant token consumption across agent calls.

OPERATIONAL FIT

You serve technical consulting firms that need to preserve client-specific architectural context and decision rationale across project handoffs.

Skip If

4
DEAL BREAKER

Your client base is non-technical (marketing agencies, design shops, e-commerce) where code repositories and engineering tools are not in use.

CAUTION

You need transparent, predictable per-seat pricing to calculate client retainer margins; Oynix requires enterprise sales conversations with no published rate card.

CAUTION

Your clients use Slack or Microsoft Teams as their primary knowledge repository and lack structured GitHub/Jira/Confluence workflows to index.

CAUTION

You require a white-label client portal; Oynix does not publish a white-label offering and client-facing surfaces display the Oynix brand.

Bottom Line

Oynix builds a searchable knowledge graph from GitHub, Jira, Confluence, and 15 other engineering tools, then surfaces that context to AI agents via MCP without token consumption. It's purpose-built for software development agencies, engineering teams, and AI agent development shops that need to preserve institutional knowledge across distributed codebases and decision logs. The local-first architecture and zero-token retrieval make it a strong fit for agencies billing clients on retainer for AI-assisted development work. However, pricing is enterprise-only and seat-based, so resale economics depend on your client's team size and whether you can justify the seat cost against the productivity gain.

Reality Check

Trade-offs & Gotchas

Oynix requires contact-sales pricing negotiation with no published per-seat rate, making it difficult to forecast client retainer margins upfront. The tool's value is highest for teams with mature GitHub/Jira/Confluence workflows; agencies serving clients with fragmented or legacy tooling may struggle to justify the implementation effort.

Implementation Reality

Moderate effort: standard configuration with some customization needed

Effort: 4/10Time: 4/10

Academy for Oynix

Work through it in order: the course for this service first, then the modules behind it.

Core concepts

The mental model you need to price and scope the work.

  1. Wiring Over WidgetsConcept

    The AI agent itself is a commodity, but the value for agencies lies in the integration layer: connecting a pre-built agent to a client's CRM, calendar, and review cycle. This framework shifts focus from selecting the 'best' agent to mastering the wiring process. For example, an agency using Vendasta's white-label AI receptionist for a local business must configure it to match the client's booking rules and follow-up cadence, turning a generic tool into a tailored service. As agentic AI adoption grows (77% of decision-makers now run agents in production), clients expect this customization. Agencies that treat agents as components and invest in repeatable wiring processes can charge retainers for ongoing optimization, rather than one-off setup fees.

  2. Wiring Over WidgetsConcept

    The AI agent market sells finished workers, but the strategic value for agencies lies not in the agent itself, which is increasingly a commodity, but in the wiring that connects it to a specific client's CRM, calendar, and review cycle. This framework, 'Wiring Over Widgets,' argues that agencies that treat agents as components rather than products win. The agent is the widget; the wiring is the integration, customization, and ongoing optimization that turns a generic tool into a tailored solution. For example, a white-label platform like Vendasta provides AI employees, but the agency's role is to configure them for each local business's unique lead flow and follow-up process. This wiring is where retainer pricing originates, as it requires ongoing maintenance and adjustment. Recent research shows that 88% of B2B marketers face foundational gaps, meaning clients need help not just deploying agents, but ensuring their operations can support them. Agencies that master the wiring can charge a premium for the irreducible value they add.

  3. Integration MoatConcept

    The Integration Moat framework holds that the durability of an AI agent engagement is determined by how deeply the agent is wired into a client's existing systems, not by the agent's underlying capability. Since the agent itself is increasingly a commodity, the switching cost for the client lives in the integrations: the CRM fields mapped, the calendar sync, the review-cycle triggers, and the exception-handling rules. Agencies that invest in this wiring create a moat that competitors offering generic agents cannot cross. For example, a white-label platform like Vendasta lets an agency deploy an AI receptionist for a local business, but the real value is in configuring it to the client's booking flow and follow-up cadence. With 77% of AI decision-makers now running agentic AI in production, clients expect this depth, and agencies that deliver it convert one-off projects into retainers.

8 modules selected for Oynix

Frequently Asked Questions

Answers about pricing, setup, implementation

Oynix is an engineering memory layer that builds a searchable knowledge graph from code repositories and team collaboration tools like GitHub, Jira, and Confluence. It provides zero-token retrieval for AI agents via MCP, generates a self-writing wiki from team decisions and code, and returns answers with source attribution. The tool syncs data from 18 connectors and can run locally on a developer's machine or on a shared server for team collaboration.

Oynix pricing is custom and enterprise-only. The Local + Server plan includes every connector, better embeddings and reasoning, Oynix Ask, Oynix Wiki, and shared memory for the whole team. Contact sales for a quote based on your team size and deployment model.

No verified white-label program. Client-facing surfaces show the Oynix brand, so you cannot present a fully branded portal to end clients. You can resell Oynix as a managed service under your own brand, but the underlying tool will display Oynix branding in the UI.

Yes. Oynix natively supports GitHub, GitLab, and Bitbucket as connectors. It also integrates with Jira, Linear, Slack, Microsoft Teams, Notion, Confluence, Google Docs, Microsoft 365, and 8 additional platforms including Sentry, Firebase, Supabase, Mixpanel, and CleverTap.

Setup time depends on the number of repositories and tools to index. Initial connector configuration typically takes 15-30 minutes per tool. Full knowledge graph population (GitHub history, Jira backlog, Confluence pages) can take hours to days depending on data volume, but indexing runs in the background without blocking team access.

Oynix is designed for software development agencies, engineering teams, AI agent development teams, and technical consulting firms. It's most valuable for clients with mature codebases, active GitHub/Jira/Confluence workflows, and teams using AI-assisted development tools like Claude Code or Cursor.

Yes. Oynix can run locally on a developer's machine for free, with no external dependencies or cloud connectivity required. For team collaboration, it can run on a shared server within your network. This makes it suitable for agencies serving clients with strict data residency or security requirements.

Oynix operates as an MCP server, meaning AI agents retrieve context directly from the local knowledge graph instead of making API calls to external services. This eliminates token overhead for context retrieval, making AI-assisted development cheaper and faster. Session outputs are automatically re-indexed, so each task makes future tasks more efficient.