SAAS TOOLS
| SaaS | URL | Category | Features/Notes |
|---|---|---|---|
| Arch-Router | https://huggingface.co/katanemo/Arch-Router-1.5B | LLM Routing | Preference-based routing via plain language rules; 1.5B params; plug-n-play with any LLM endpoints; SOTA query-to-policy matching; cost/latency optimization |
| Arch | https://github.com/katanemo/archgw | AI-native proxy | Hosts Arch-Router; designed for AI agents |
USER NEEDS
Pain Points:
- Embedding-based routers are brittle and can't handle multi-turn conversations or fast-changing requirements
- Performance-based routers rely on benchmarks that don't reflect real-world subjective quality
- Difficulty adapting routing to new models or features without retraining
- Complex routing logic becomes unmanageable with if/else statements
Problems to Solve:
- Need for flexible LLM routing that adapts to conversational context and topic shifts
- Ensuring routing decisions align with human-judged quality criteria (e.g., legal compliance, brand tone)
- Simplifying model updates and policy changes without system retraining
- Optimizing cost and latency by routing queries to appropriate models
Potential Solutions:
- Using plain-language preference rules for routing (e.g., "contract clauses → GPT-4o")
- Auto-regressive router models that map prompts to policies without retraining
- Plug-and-play architecture supporting any LLM endpoint
- Cost/latency-aware routing decisions
GROWTH FACTORS
Effective Strategies:
- Co-designing products with established companies (e.g., Twilio, Atlassian) for real-world validation
- Open-sourcing models and code to build credibility and community adoption
- Publishing research papers to establish technical authority
Marketing & Acquisition:
- Showcasing technical superiority (e.g., "fastest LLM router", SOTA performance)
- Highlighting ease of integration (plug-n-play, zero retraining)
- Leveraging GitHub and Hugging Face for developer outreach
Monetization & Product:
- Offering tiny footprint (1.5B params) enabling low-cost deployment
- Focusing on adaptability to customer-specific preferences
- Solving acute pain points in LLM routing space
User Engagement:
- Providing accessible resources (GitHub repo, Hugging Face model, arXiv paper)
- Emphasizing practical benefits like cost/latency optimization
- Targeting enterprise use cases through partnerships