Choosing an AI development partner isn’t just a checkbox item. It’s a high-stakes decision that can either accelerate your roadmap or derail it.
I’ve seen both outcomes. One led to a fast MVP launch. Another? Weeks of cleanup after a failed chatbot that couldn’t even recognize product names.
So, if you're evaluating an AI development company, here’s a no-nonsense checklist to help you separate serious players from surface-level vendors.
1. Go Beyond the Demo
Flashy demos don’t equal real-world performance. Many vendors showcase slick prototypes built with open-source models and zero scalability in mind. That’s fine—until it’s not.
Ask for production use cases, not proof-of-concepts. Ask what models they deployed, what tradeoffs they made, and how their solution performed under load.
If they can’t explain it clearly or brush off your technical questions, move on.
2. Evaluate Their Process, Not Just Output
A solid AI partner should walk you through their process:
Data collection and cleaning
- Model selection and iteration
- Evaluation metrics
- Deployment strategy
- Monitoring and retraining plans
Ask for documentation. Ask how they handle model drift, bias, and privacy. If they’re vague or dismissive, that’s your red flag.
3. Inspect the Team Behind the Pitch
Don't get sold by salespeople. Ask to speak with the actual engineers.
Request GitHub profiles, research papers, or open-source contributions. Look for team diversity too: ML engineers, backend devs, data scientists, and domain experts should all be in the mix.
A team that understands both the tech and the business side of your domain will deliver better results—faster.
4. Domain Knowledge Matters More Than You Think
A general-purpose AI firm may not cut it. If you're in healthcare, fintech, or logistics, you need a team that speaks your language.
They should understand compliance, risk, and business KPIs specific to your world—not just throw around terms like “transformers” or “autoencoders.”
5. Look for Transparency and Support
How often do they report progress? Do they work in agile sprints?Do they support tools like Jira, GitLab, or Slack?
Bonus points if they share onboarding and support processes up front. One of the best partners I worked with sent over their full support playbook before we signed. That told me everything I needed to know.
6. Nail the Contract
Before you commit, get the legal side right:
- Who owns the data and models?
- How is performance defined and measured?
- What does support include—and for how long?
- What happens if results don’t meet expectations? Get it all in writing. No assumptions. No vague promises.
The bottom line: Good AI partners are clear, accountable, and technically sharp. They’ll talk you through tradeoffs, show their work, and stick around after deployment.
The rest? Just noise.
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