An AI MVP is not a smaller version of your final product. It is the cheapest honest test of one question: does this AI capability create enough value, with your data and your users, to justify building more? Done well with a nearshore partner, it can reach production in 30 to 60 days. Done badly, it becomes a demo that never ships. Here is how to plan one.
Define the one question the MVP must answer
Start with a single workflow and a measurable outcome. Examples: reduce the time to triage inbound support requests, draft first-pass proposals from past ones, extract fields from invoices into your ERP, or give your sales team an assistant that answers questions from internal documents. Pick something where the process already exists, the data is reachable and someone owns the result.
Write the success criteria before any design work: for instance, “at least 70 percent of drafts accepted with minor edits (a hypothetical target)” or “average handling time reduced by a defined amount on a sample of 200 cases.” Choose the numbers with your team; the point is that they are agreed in advance.
A realistic timeline: 30 to 60 days
The range depends mostly on data access and integrations, not on model work. A typical shape:
- Week 1, discovery: map the workflow, review sample data, confirm access, define success metrics, write the scope.
- Weeks 2 to 3, prototype: build the core AI step against real examples, review outputs with your team, adjust prompts, retrieval or rules.
- Weeks 4 to 6, integration and hardening: connect to your systems, add guardrails, logging, error handling and a simple interface or dashboard, deploy to staging.
- Weeks 6 to 8, pilot in production: run with real users on a limited volume, measure against the criteria, collect feedback.
Projects with clean APIs and clear data can land near 30 days. Legacy systems, scattered documents or approval-heavy environments push toward 60. Be skeptical of anyone promising a production-ready system in two weeks.
Budget: what to expect
For a US company working with a senior nearshore partner, an AI MVP is usually scoped as either a fixed-price project or a short monthly engagement. Ongoing nearshore engagements with senior leadership typically start around USD 8,000 per month, and a well-defined MVP is often delivered in one to two months of that kind of effort, or as a fixed-scope project priced from a written assessment. Your actual number depends on the number of integrations, the sensitivity of the data and how much specialist work is needed.
Plan for costs beyond development: model usage fees, cloud hosting, any third-party tools and your own team’s time for reviews. For most MVPs, run costs are modest compared to build costs, but estimate them with realistic volumes rather than demo volumes. Ask your partner for a simple projection at 1x and 10x usage.
Staffing model: a lead plus specialists
A sensible MVP does not require a large team. The model that works is a senior lead who is accountable for the whole project, who handles architecture, scope and communication, and who brings in vetted remote specialists when needed, for example someone for data pipelines or a front-end developer for the interface. You pay for the expertise the work requires, when it is required.
Communication is structured: a preview of what will be built before each stage, weekly check-ins, written reports by email or a progress dashboard and a staging environment you can test at any time. This is what keeps a distributed project predictable.
The main risks, and how to reduce them
- Data not ready. Documents are scattered, inconsistent or inaccessible. Mitigation: sample the data in week one, before committing to a design.
- Scope creep. The MVP turns into a platform. Mitigation: one workflow, one user group, a written out-of-scope list.
- Accuracy that is good in demos and weak in practice. Mitigation: test on a set of real cases you label together, and keep a human review step at first.
- Privacy and security. Sensitive data goes to the wrong place. Mitigation: decide early what data may reach external models, use appropriate vendor agreements, apply access controls and log activity. Consider your obligations under laws such as state privacy statutes, HIPAA or other sector rules, and involve your counsel.
- No adoption. The tool works but nobody uses it. Mitigation: involve the actual users from the first week and fit the output into the tools they already use.
- Vendor lock-in. Mitigation: own the repositories and cloud accounts, require documentation and keep model providers swappable where practical.
Decide what happens after the MVP
The end of the pilot should produce a decision, not a drift. Meet with a short scorecard: results against the success criteria, issues found, cost to operate, and a recommendation to scale, adjust or stop. Stopping is a legitimate and valuable outcome; you spent a small amount to learn that something would not pay off.
If the answer is to scale, you will already have working code, a staging environment, documentation and a team that knows your business, which is a far better starting point than a slide deck.
Questions to ask a prospective partner
- Who is the accountable lead, and which specialists would you bring in?
- What do you need from us in the first week?
- How will you show progress between meetings?
- What is explicitly out of scope?
- Who owns the code, the prompts and the cloud accounts at the end?
An MVP is a disciplined experiment. With a clear question, a small scope and a communication process built for distance, a Brazilian partner can get you to a real answer quickly and at a sensible cost.
Want to put this to work? Convertty runs an AI assessment of your workflows and delivers an implementation plan with a pilot in production in 30–60 days. Book a call.
