20/08/2026
Choosing a Gen AI development partner for an enterprise project? Use this practical checklist to guide conversations and compare options.
- Capabilities and scale: What Generative AI capabilities do you offer (AI agents, NLP, ML), and can you scale to enterprise workloads and cloud-native architectures? Do you have a track record delivering MVPs to production at scale?
- Governance, risk, and ethics: How do you handle model governance, bias mitigation, explainability, regulatory compliance, data privacy, and audit trails?
- Security and data handling: What security controls are in place (encryption, IAM), how is data protected in transit and at rest, incident response, data localization, and vendor risk management?
- Engagement models and IP: What engagement models do you support (project, product engineering, staff augmentation)? Who owns IP and model artifacts, and how are pricing, timelines, and success metrics defined?
- Delivery, operations, and integrations: What DevOps/MLOps practices do you use, how do you monitor models post-deployment, and how do you approach API integration and legacy system modernization?
If this resonates, what other questions do you rely on when evaluating a Gen AI partner? Share below or DM to discuss your enterprise goals and constraints.