7 Questions to Ask Before Hiring an AI Development Partner
Hiring a software development company is already difficult when you are not technical. Hiring an AI development partner adds another layer of uncertainty.
Almost every vendor can talk about LLMs, AI agents, RAG, automation, and the models they work with. Portfolios can look equally convincing. A prototype shown during a sales call may work beautifully.
However, none of this necessarily tells you how well the team will handle your project once it meets factors like real users, imperfect business data, and security requirements.
With all that, you don’t need to become an AI engineer to evaluate AI engineers. Instead of trying to judge their technical knowledge directly, ask questions that reveal how they make decisions. Here are seven questions worth asking before you hire an AI development firm.
It may sound strange at first, but it makes sense. When you approach a vendor with an idea for an AI solution, there's a natural incentive to accept the premise and move straight into implementation.
A good development partner should first validate whether the idea makes sense in the first place. That means understanding the business problem, reviewing the existing process and data, and determining whether AI would improve the outcome. Some problems are better solved with conventional software, workflow automation, existing SaaS tools, or simple business rules.
Adding AI where it isn’t needed can introduce extra cost, complexity, and uncertainty without creating enough additional value to justify them. That's why technology selection should follow problem definition, not precede it. Before discussing models, agents, or architecture, a potential partner should be able to explain what AI would contribute to your particular use case and why it is a better fit than simpler alternatives.
Pay attention to how potential AI development partners discuss those alternatives. You're not looking for a company that can put AI everywhere. You're looking for one that knows when AI is worth using and when it isn’t.
Building a proof of concept can demonstrate that an idea is technically feasible. Production software has to deal with considerably more: authentication, permissions, integrations, monitoring, unexpected inputs, security, performance, failure handling, user feedback, and ongoing maintenance.
Also, don’t make the mistake of evaluating experience only by industry. Suppose you’re building an AI system that extracts information from insurance documents. A vendor that has never worked in insurance but has delivered production document-processing systems for financial or logistics companies may have more relevant experience than a vendor whose only insurance project was a simple chatbot.
Ask what was technically similar about previous projects, what changed between the prototype and production versions, and what the team learned once real users started using them. A strong answer is usually specific. The company should be able to explain the business problem, architecture choices, constraints, results, and tradeoffs without hiding behind a list of technologies.
“Is our data secure?” is too broad a question. Ask where your data will go:
Surely, you don’t need to quiz vendors on security terminology. Ask them to explain your data flow and major risks in language you understand. The answers will depend on your architecture and providers, but the partner should be able to map the data flow clearly.
When comparing AI development proposals, don’t look only at the initial development estimate. You also need to understand what the solution will cost to run and maintain after launch.
AI applications can have ongoing costs for model or API usage, cloud infrastructure, data storage and processing, monitoring, third-party services, maintenance, and support. Some of these costs are usage-based, which means they can increase as more people use the system or as the volume of requests grows.
The architecture also has a direct impact on cost. For example, not every task needs the largest or most expensive AI model. A well-designed system can use different models for different tasks, limit unnecessary data processing, and optimize how often AI services are called.
That’s why a potential partner should be able to explain the main cost drivers behind their proposed solution and how those costs could change as usage grows. They may not be able to give you an exact monthly figure before the architecture and expected usage are defined, but they should be able to explain what you will be paying for, and which technical decisions can affect that cost.
The goal isn’t simply to build the cheapest AI solution. It’s to design one where cost, performance, reliability, and business value remain reasonable as the system moves into production and scales.
AI-assisted software development can speed up coding, documentation, testing, analysis, and other parts of the software development lifecycle. That can be beneficial to the client. However, faster code generation shouldn’t mean weaker engineering accountability. At Univisia, for example, our UNITED delivery approach integrates AI throughout the development lifecycle while senior engineers remain accountable for architecture, security, quality, and business outcomes. Whatever approach a prospective partner uses, ask where human review and responsibility sit. The question isn’t whether developers use AI. Increasingly, many will. The better question is whether the delivery process gives you the benefits of AI-assisted engineering without outsourcing important technical decisions to AI itself.
AI projects don’t end when the application starts returning responses in production. Real users phrase requests differently from test users, business data changes, external models and services evolve, and eventually, a workflow that performed well during testing can encounter cases the development team never saw.
That’s why monitoring should cover more than whether the server is running. Depending on the application, teams may need visibility into latency, errors, token consumption, failed tasks, user behavior, response quality, tool calls, or other application-specific measures.
For example, Microsoft recommends designing AI observability from the beginning and describes evaluation, monitoring, and tracing as complementary parts of production AI observability.
Ask who reviews those signals and what happens when performance deteriorates as well as how improvements are evaluated before being released. Changing a prompt, model, retrieval strategy, or tool configuration may improve one type of request while making another worse. A test dataset and defined evaluation criteria make those changes easier to assess systematically. The founder-friendly version of this entire conversation is simple: If this system becomes worse six months from now, how will we know? A good development partner should have an answer.
Nobody likes discussing the end of a partnership before it has begun. Do it anyway. Essentially, you should understand how dependent you will be on the company that developed it. Source-code ownership is only one part of this. Depending on the project, you may also need access to:
Ask what gets transferred to you, what remains the vendor’s intellectual property, which third-party components have their own licensing conditions, and whether another competent development team could realistically maintain the system.
Some vendor dependency isn’t inherently bad. Managed services and proprietary components can be reasonable architectural choices when their advantages justify them. The problem is unexpected dependency. You should understand which parts of your product you own, which parts you rent, and which parts would be difficult to replace before those decisions become expensive to reverse.
Hiring an AI development partner doesn’t require becoming an expert in models, embeddings, RAG pipelines, or agent frameworks. Your job is to understand the business problem and ask questions that expose how the potential partner approaches everything you cannot easily evaluate yourself: feasibility, quality, failure, security, cost, architecture, production readiness, and long-term ownership.
The answers should leave you with a clearer understanding of the project, including its limitations and tradeoffs. If every answer somehow ends with “AI can handle that,” keep looking.
If you have an AI use case but aren’t yet sure what should be built, Univisia offers a free AI assessment session to review the business problem, existing processes and data, technical feasibility, and potential implementation options before you commit to development – book yours now.