Research Acceptance Criteria

Acceptance Criteria

🚦 Maturity Level

Is the technology in alpha, beta, or GA (general availability)?
Are there production use cases or just academic papers?
Is it actively maintained and developed with a clear road map?
✅ Accept if: It has a stable release or strong momentum with a credible roadmap.

💡 Innovation Value

Does it introduce something new or significantly better (e.g., speed, accuracy, efficiency)?
Is it a novel approach or the evolution of existing tech?
✅ Accept if: It demonstrates clear differentiation or solves real-world problems in a new way.

🧩 Integration Readiness

Is it easy to integrate into existing stacks (e.g., APIs, SDKs, CLI, IDE Extension)?
Does it support common deployment targets (cloud, edge, on-prem)? Does it have built-in extension points for Enterprise Monitoring, Rate Limiting, Error Logging? ✅ Accept if: Integration is reasonably straightforward, with standard tooling.

📚 Documentation & Developer Experience

Is the documentation comprehensive and up to date?
Are there actively maintained tutorials, examples, and community support?
Does it follow familiar dev practices (e.g., RESTful API, GitHub presence)?
✅ Accept if: It’s developer-friendly and well-documented.

🛠️ Tooling & Ecosystem

Does it work well with popular tools and frameworks (e.g., LangChain, Hugging Face, PyTorch, Kubernetes)? Is it supported in all major AI languages (Python, NodeJS/Typescript, C#, Java)? Is there an ecosystem or marketplace? ✅ Accept if: It plays well in the broader AI/ML ecosystem.

🔐 Security & Privacy

Does it handle data securely?
Does it comply with relevant standards (e.g., GDPR, HIPAA, SOC 2)?
Are there risks related to model misuse or hallucination? Is the technology delivered through an App Store/Marketplace with an acceptable level of trust? Are there known attack vectors to be aware of (e.g., tool poisoning, data exfiltration vulnerabilities)? Is data stored and used for AI training without the user's consent? ✅ Accept if: It follows best practices and has a transparent risk profile.

💼 Commercial & Licensing Viability

Is it open-source or proprietary?
What are the licensing costs or limitations (e.g., MIT, Apache, commercial)?
Is there a sustainable business model or vendor behind it?
✅ Accept if: Licensing is clear, and it’s feasible for enterprise or dev team adoption.

🧠 Use Case Fit

Does it align with the business or technical priorities of your audience (e.g., NLP, LLMOps, model serving)?
Can it be used for Enterprise client delivery? or only for internal innovation? ✅ Accept if: It maps to current or emerging needs of developers and IT consultants.

📈 Performance & Benchmarking

Is it performant in real-world enterprise scenarios (latency, cost, scalability)?
Are there benchmarks or comparisons with similar tools/models?
✅ Accept if: It meets or exceeds the performance of current tools in its category.

🌐 Community & Adoption

Is there a community of users contributing to or discussing the tech?
Are companies or research orgs using it in production or pilots?
✅ Accept if: There’s meaningful traction or early adoption.

🔋 Responsible AI

Does technology follow these principles?Fairness Reliability and Safety Transparency Accountability Inclusiveness