A proof of concept for a generative AI application can be built in two weeks. A production-grade generative AI system that handles real user inputs reliably, safely, and at acceptable cost is a four to nine month engineering project. The evaluation criteria for a generative ai development services partner must reflect this reality – not the demo timeline.
How They Define PoC Scope
The first thing to evaluate is how a prospective generative AI development partner defines the PoC scope. A partner oriented toward production delivery will define a PoC that validates the specific technical hypothesis at risk – typically, whether the model’s outputs meet the quality bar required for the use case using representative production data. A partner oriented toward winning the engagement will define a PoC that generates an impressive demo. Ask specifically: what will the PoC use as input data, how will success be measured, and what architectural decisions will the PoC validate? The answers reveal whether the partner is building toward production or building toward a sales conversation.
Model Selection Expertise
The choice of foundation model – GPT-4o, Claude Sonnet, Gemini Pro, Llama, or domain-specific models – significantly affects both output quality and cost for a given use case. A credible generative AI implementation partner will benchmark multiple models against your specific use case requirements, not default to a single model because it is familiar. For RAG-heavy applications, retrieval quality and context window size are the primary model selection criteria. For code generation, benchmark performance on your specific stack matters. For document summarization, output consistency and hallucination rate on domain-specific content is the relevant metric.
Safety and Content Evaluation
Generative AI applications have a failure mode that other software categories do not: they can produce outputs that are harmful, misleading, or off-brand in ways that are difficult to predict from test cases alone. A responsible generative ai development services partner will include safety evaluation as a defined deliverable – red-teaming the model against adversarial inputs, evaluating output consistency across edge cases, and implementing content filtering appropriate to the application’s deployment context. Partners that skip this step are delivering a system that will fail visibly in production.
Production Handover and Cost Modeling
Ask every prospective partner two questions before signing: What does production handover include, and what will the system cost to operate at the expected query volume? Partners who cannot answer the second question with a model that accounts for inference costs, retrieval costs, embedding costs, and monitoring overhead are partners who have not built generative AI applications at production scale. LLM inference costs are variable and can be significantly higher than initial estimates if query volume, prompt length, or model selection are not carefully modeled. The right generative AI development partner will provide this cost model as part of the PoC deliverable, not as an afterthought after the engagement has started.