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Questions from Miami startup product teams.

What does a competitive AI feature analysis cover. How do you evaluate what competitors are doing?

A competitive AI feature analysis covers four dimensions. First, capability inventory: a documented list of every AI feature each competitor offers, the entry point in their product (where the user encounters it), and the user-facing description of what it does. This is gathered through product trials, marketing pages, release notes, and job postings that reveal what teams they are building. Job postings for AI engineers with specific skills indicate what model approaches a competitor is pursuing 6–12 months before a feature ships. Second, quality assessment: for AI features that can be tested directly, a structured evaluation using the same inputs across competitors to compare output quality, latency, and fallback behavior. Third, build/buy signal: patent filings, acquisition history, and engineering blog posts indicate whether a competitor built their AI feature internally or integrated a vendor. This informs your own build/buy decision. Fourth, gap analysis: the intersection of features competitors have shipped, features they are visibly building, and features that neither they nor you currently offer. The gap analysis is the most valuable output because it identifies durable differentiation opportunities rather than catch-up features.

How do you include Spanish-language performance requirements in an AI product spec?

Spanish-language performance requirements belong in the same section of the spec as the English performance requirements, not in a localization appendix. The spec defines the evaluation framework for the Spanish-language version with the same rigor as the primary version: an accuracy threshold, a labeled test set in Spanish, and a latency requirement that accounts for longer average token lengths in Spanish text. Three specific requirements are non-negotiable for Miami market deployments. First, the test set must be sourced from Miami Spanish speakers, not from Spain or Mexico City Spanish. Regional vocabulary differences affect model accuracy in ways that a generic Spanish test set does not capture. Second, the accuracy threshold for Spanish must be set independently from the English threshold, not assumed to be equal. Most multilingual models perform 5–15 percentage points lower on Spanish than English for domain-specific tasks. Third, the spec must define what happens when a user switches language mid-session: does the model context carry over, and is the AI output in the same language as the user's input or the account's default language?

What is the right scope for a first AI product spec. How do you avoid over- or under-specifying?

A first AI product spec is over-specified when it describes the model architecture, the training procedure, or the specific model provider. Those are implementation decisions that belong to engineering. A spec that dictates the model is a spec that will be wrong by the time it is implemented. A first AI product spec is under-specified when it describes the feature's outputs without defining the evaluation criteria, when it describes the happy path without a failure mode section, or when it describes the user workflow without defining what happens when the AI is wrong or uncertain. The right scope covers: the use case and the user problem being solved, the inputs the feature accepts and the outputs it produces, the accuracy threshold and the evaluation methodology, the failure mode matrix, the monitoring spec, and the build/buy/fine-tune decision with the rationale. For a Miami startup shipping a first AI feature, the spec should be completable in four to six hours of writing. A spec that takes longer usually means the product decision has not been made yet and the spec is being used to avoid making it.

How do you decide whether to build a bilingual AI feature with multilingual models vs. separate models per language?

The decision between a multilingual model and separate per-language models depends on four factors. First, domain specificity: for highly domain-specific tasks such as legal document analysis, medical coding, or financial services terminology, separate fine-tuned models typically outperform multilingual general models. The performance gap widens as the domain narrows. Second, data volume: fine-tuning a Spanish-specific model requires a labeled Spanish dataset of sufficient size. If the company does not have 500 to 1,000 labeled Spanish examples for the specific task, fine-tuning produces a model that is worse than the multilingual baseline. Third, operational complexity: two separate models require two separate monitoring pipelines, two separate evaluation processes, and two separate update cycles. This is a product maintenance cost that grows over time. Fourth, quality threshold: if the multilingual model meets the accuracy threshold defined in the spec for both English and Spanish independently, separate models are not justified. Test this before committing to the separate-model architecture. Most Miami startup AI features do not require separate models. The multilingual model approach is correct for the majority of use cases until the company has sufficient labeled data and engineering capacity to justify the operational overhead.