Hiring a Lovable expert: what to look for and what to ask

A convincing interface can now be produced quickly. That is useful, but it is not the same as building a product that protects data, survives failure, supports real operations, and advances a commercial goal.

The expert you need depends on the job. A visual prototype may require strong product and interaction judgment. A customer-facing application also requires data modelling, access control, server-side boundaries, testing, launch operations, and measurement.

A portfolio screenshot demonstrates taste. A live product demonstrates the ability to finish. Ask to see sign-in, real data states, errors, mobile behaviour, and a workflow that crosses the interface and server.

Then ask what changed between the first prototype and launch. Strong builders can describe a mistake, the evidence that exposed it, and the trade-off they made. Vague perfection is less credible than specific learning.

You do not need to conduct a technical examination. Give the candidate a realistic situation: two customer organisations use the same application, each has admins and members, and an AI feature calls a paid model. Ask how they would prevent one customer from seeing another's data and where the model key would live.

A credible answer should describe identity, server-side authorisation, row-level data rules, separate roles, validated input, protected secrets, and tests using different accounts. Be cautious if the answer focuses only on hiding screens or trusting values stored in the browser.

Many consultancies sell with their most experienced person and deliver through a different team. That can be appropriate if the model is transparent and the handoffs are well managed. It is a problem when the person learning the business disappears after the contract is signed.

Ask who writes the product specification, makes architecture decisions, reviews generated code, talks to users, and responds when a release fails. The answer should identify accountable people rather than a generic team.

The client should understand who controls the source code, domain, database, cloud accounts, analytics, email systems, and billing. Avoid arrangements where an essential account exists only under the consultant's identity or where departure makes the product inaccessible.

Ownership does not eliminate the need for support. It ensures support remains a choice. Ask for concise documentation and a handover path appropriate to the product's complexity.

Feature lists create a false sense of comparability. One proposal may include more screens while avoiding the hardest uncertainty. Compare how each partner defines success, reduces risk, sequences the work, and handles discoveries.

A useful proposal states what is known, what remains uncertain, the result of each phase, the client's responsibilities, exclusions, ownership, and the conditions that could change scope or price.

No single phrase disqualifies a provider, but repeated vagueness around production, ownership, and outcomes should change the decision. Slow down when a proposal promises certainty before discovery or treats AI generation as a substitute for review.