Orchex Target Audience

Version: 3.0 Last Updated: 2026-02-23 Purpose: Define who we're building for and how to reach them

Primary Audience: Power Users of AI Coding Assistants

Profile

• Who: Developers using Claude Code, Cursor, or Windsurf daily who hit rate limits, serial execution bottlenecks, and file conflicts on large tasks
• Size: Millions of developers using AI coding assistants daily
• Budget: Already paying for LLM API access and/or IDE subscriptions
• Need: Parallel execution at scale with file safety — their AI assistant needs an engine for large tasks

Segments

1. OpenAI Power Users (Maya)

• Uses GPT-4/GPT-4.5 via API for development
• Frustrated by lack of native orchestration tooling
• Wants parallel execution with their preferred model
• Pain: "OpenAI doesn't have anything like parallel agents"
• Value: Finally, production-grade orchestration for GPT

2. Gemini Developers (Kai)

• Google Cloud ecosystem, uses Gemini 2.0 for coding
• Needs reliable, repeatable automation
• Values enterprise-grade reliability
• Pain: "I need orchestration that works with my stack"
• Value: Multi-model support with ownership enforcement

3. Local LLM Enthusiasts

• Privacy-conscious, uses Ollama or LM Studio
• Air-gapped or offline development needs
• Values self-hosting and data sovereignty
• Pain: "Cloud-based orchestrators don't support local models"
• Value: Run agents offline with full control

Primary Audience: Solo Developers

Profile

• Who: Individual developers building products independently
• Size: Largest segment, high viral potential
• Budget: Price-sensitive, value BYOK model with any provider
• Time: Limited — speed and reliability matter

Segments

1. Indie Hackers

• Building SaaS products, micro-startups
• Ship fast, iterate based on user feedback
• Active on Twitter/X, Indie Hackers forum, HackerNews
• Pain: "I need parallel agents that work with my LLM of choice"
• Value: Speed to market, more features per weekend, provider flexibility

2. Side Project Builders

• Full-time job + side projects
• Weekends and evenings only
• Want maximum output from limited time
• Pain: "Sequential AI sessions are too slow, and I want to use GPT not Claude"
• Value: Parallel execution with any LLM = more done in less time

3. Freelancers / Consultants

• Bill by project or deliverable
• Faster delivery = better margins
• Need reliable, repeatable workflows
• Pain: "AI is fast but unreliable, and I can't trust it with my files"
• Value: Self-healing + ownership enforcement = reliable delivery

How to Reach Solo Devs

Secondary Audience: Small Teams (2-5 developers)

Profile

• Who: Startup engineering teams, small agencies
• Size: Smaller segment, higher LTV potential
• Budget: Budget-conscious but can pay for value
• Needs: Consistency, provider flexibility, file safety

Segments

1. Early-Stage Startups

• Pre-seed to Series A
• Moving fast, limited runway
• Need to ship before competitors
• Pain: "We're using different LLMs and have no consistency"
• Value: Provider-agnostic orchestration with ownership enforcement

2. Small Agencies / Studios

• Client work with deadlines
• Multiple projects in parallel
• Need predictable delivery
• Pain: "AI agents modify files they shouldn't touch"
• Value: Ownership enforcement + self-healing = reliable delivery

3. Open Source Maintainers

• Managing projects with contributors
• Need reproducible workflows
• Often resource-constrained
• Pain: "Contributors use different AI tools inconsistently"
• Value: Defined orchestration patterns that work with any LLM

How to Reach Small Teams

Not Targeting (Yet)

Enterprise (10+ developers)

Why not now:

• Long sales cycles (solo dev can't support)
• Complex regulatory compliance needs
• Custom integration requirements
• Need dedicated support team

When to revisit: After 500+ users, when data shows enterprise interest

Agencies with Large Teams

Why not now:

• Need project management features
• Multi-tenant requirements
• Custom billing arrangements

When to revisit: After Team tier proves product-market fit

Claude-Only Users Satisfied with Serial Execution

Why not now:

• They work on small tasks (1-3 files) where serial execution is fine
• Don't hit rate limits or context degradation

When to revisit: When their tasks grow beyond 5-10 files and they hit Claude's walls (rate limits, context compression, no file safety)

Non-Technical Users

Why not now:

• Orchex requires understanding of code structure
• MCP configuration is technical
• No-code tools are a different market

When to revisit: Never (not our market)

Persona Details

Persona 1: Maya the OpenAI Power User

Demographics:

• Age: 25-40
• Location: Global (English-speaking)
• Role: Full-stack developer / ML engineer
• Experience: 3+ years with AI APIs

Situation:

• Uses GPT-4/GPT-4.5 via API for development
• Frustrated by lack of orchestration tooling for OpenAI
• Sees Claude users with Agent Teams and wants similar capability
• Has tried building custom orchestration, found it complex

Goals:

• Parallel agent execution with GPT models
• Reliable, repeatable automation
• File safety — agents shouldn't modify arbitrary files
• Provider flexibility for future changes

Frustrations:

• No native parallel orchestration for OpenAI
• Custom solutions are fragile and hard to maintain
• Agents can break code by modifying wrong files
• Error handling is tedious

Trigger Events:

• "Why can't I run parallel agents with GPT like Claude users can?"
• "My agent just overwrote a file it shouldn't have touched"
• "I need orchestration that works with MY model"

Discovery Path:

1. Searches for "parallel agents OpenAI" or "GPT orchestration"
2. Sees Reddit post or Twitter thread about orchex
3. Reads about multi-LLM support, clicks through
4. Tries npm install -g @wundam/orchex with OPENAI_API_KEY
5. First orchestration with ownership enforcement → becomes advocate

Messaging That Works:

• "The orchestrator that works with YOUR LLM"
• "OpenAI, Gemini, Claude, or Ollama — you choose"
• "Ownership enforcement — streams can only modify their declared files"

Persona 2: Alex the Indie Hacker

Demographics:

• Age: 28-40
• Location: Global (English-speaking)
• Role: Solo founder / developer
• Experience: 5+ years coding

Situation:

• Building a SaaS product on evenings/weekends
• Uses various AI tools (GPT, Gemini, Claude, Cursor)
• Has shipped products before
• Knows what good code looks like

Goals:

• Ship MVP faster with any LLM
• Maintain code quality without slowing down
• Automate repetitive AI interactions
• Focus on product decisions, not AI babysitting

Frustrations:

• Sequential AI sessions feel slow
• Different LLMs have different tooling
• Agents sometimes modify files they shouldn't
• Manual retry when things fail

Trigger Events:

• "I just spent 2 hours on what should've been a 30-minute feature"
• "My AI agent just broke my config file"
• "I wish I could run parallel agents with GPT, not just Claude"

Discovery Path:

1. Searches for "parallel AI coding" or "multi-model orchestration"
2. Sees HackerNews post or Twitter thread
3. Reads about orchex multi-LLM support
4. Tries npm install -g @wundam/orchex (free, BYOK with any provider)
5. First orchestration with self-healing → becomes advocate

Messaging That Works:

• "The orchestrator that works with YOUR LLM"
• "What took 4 sequential sessions now takes 1 orchestration"
• "Free forever for local use, BYOK with any provider"

Persona 3: Sam the Startup Engineer

Demographics:

• Age: 25-35
• Location: Tech hub or remote
• Role: Senior developer or tech lead
• Experience: 3-8 years, startup environment

Situation:

• Part of 3-5 person engineering team
• Shipping features weekly
• Team uses different AI tools (some GPT, some Gemini, some Claude)
• Responsible for code quality and file integrity

Goals:

• Ship faster without creating tech debt
• Establish consistent AI workflows regardless of LLM
• Prevent AI agents from modifying wrong files
• Focus on architecture, not implementation details

Frustrations:

• Team uses different LLMs, inconsistent tooling
• AI-generated code sometimes breaks other files
• No ownership enforcement in existing tools
• Debugging AI output is tedious

Trigger Events:

• "Our AI agent just overwrote a config file and broke production"
• "Each developer uses a different LLM with different workflows"
• "I need orchestration that enforces file ownership"

Discovery Path:

1. Team member (Maya or Alex persona) recommends orchex
2. Evaluates for team use — impressed by ownership enforcement
3. Tries locally with team's preferred LLM
4. Suggests Team tier for shared visibility
5. Becomes internal champion

Messaging That Works:

• "Provider-agnostic orchestration for your team"
• "Ownership enforcement — agents can't break what they don't own"
• "Works with GPT, Gemini, Claude, or Ollama"

Market Size (Rough Estimates)

Addressable Market Path

1. Now: OpenAI and Gemini users who want parallel orchestration
2. Next: Local LLM users wanting reliable automation
3. Later: Small teams needing provider-agnostic coordination
4. Eventually: Anyone needing multi-model orchestration with ownership enforcement

Validation Questions

Before targeting a new segment, answer:

1. Do they use a supported LLM? (OpenAI, Gemini, Claude, Ollama)
2. Do they need parallel orchestration? (Multi-file features, automation)
3. Do they need ownership enforcement? (File safety concerns)
4. Can they afford BYOK? (API costs are their responsibility)
5. Are they underserved by native tools? (No Agent Teams equivalent)
6. Can we support them? (Solo dev capacity constraint)
7. Will they tell others? (Viral potential in their community)

Messaging by Persona

End of document.

Focus on power users of AI coding assistants who hit rate limits and serial execution walls. MCP-first positioning — orchex is the engine, the AI assistant is the driver.