New AI feature development

When this is useful

A new AI feature needs more than a model call. The user task, input quality, acceptable output, cost envelope and fallback behaviour must fit the product. This entry check tests one idea against representative examples, so the decision to implement is grounded in what the feature can actually do.

• You have a candidate AI feature but no agreed definition of useful output.
• A proof of concept works on a few examples but has no product boundary.
• The team needs to choose between deterministic logic, retrieval and generation.

What you receive

• Feature brief + acceptance scenarios
• Thin prototype + evaluation notes
• Integration scope + go/no-go recommendation

What’s included

• Define one user task, its input/output contract and the review or fallback path.
• Agree a small representative test set and useful acceptance criteria.
• Build a thin non-production prototype around one existing model configuration.
• Review observed output quality, data handling and available cost/latency evidence.
• Deliver a feasibility recommendation, prototype handover and bounded implementation proposal, with one review call and one factual-correction round.

What’s outside the check

• A complete new product, multi-feature platform or production rollout.
• Custom foundation-model training, unrestricted datasets or legal certification.
• Guaranteed model accuracy, unapproved subscriptions or autonomous high-impact decisions.

Access we’ll agree

Product context, sanitised example inputs and a sandbox integration boundary. Model access and any paid usage limits are agreed before prototype execution. Never send credentials or customer datasets in the initial enquiry.

From diagnosis to a scoped fix

After evidence review, a separate fixed-price implementation can connect the feature to one agreed product surface, add evaluation and fallbacks, and document operation. Production integration is a new scope with explicit acceptance criteria.

Questions before you start