If you’re on the hook for selecting an AI development partner, you already know it’s not just about tech skills. It's about execution, clarity, and whether they actually understand what you're trying to build.
Let’s skip the shiny pitch decks and cut straight to what matters when vetting an AI vendor. Below is a checklist based on hard-earned lessons from teams that have been through it.
1. Can They Code and Deliver?
You want proof of real-world projects, not just theoretical knowledge or toy models. Ask for code samples, architecture overviews, and project retros. See if they’ve tackled problems that mirror your domain or technical stack.
Look for fluency in tools like TensorFlow, PyTorch, scikit-learn, and Hugging Face. Ask how they handled data pipelines, model retraining, and versioning. Good answers here tell you more than any case study can.
2. Do They Understand the Stack End to End?
AI isn’t just the model. It’s preprocessing, pipelines, versioning, deployment, monitoring, and scale. Does their team know how to move from notebooks to production?
Check their experience with:
Python, SQL, and Spark
Cloud platforms like AWS, GCP, Azure
CI/CD tools for ML (MLflow, Jenkins, Docker)
MLOps practices for drift detection and model health
This is where many AI companies stumble. If they can’t maintain or monitor what they build, you’ll inherit a mess.
3. What’s the Team Behind the Code?
Get past the sales team and talk to the devs. Who will be writing your code, deploying your models, and answering questions when something breaks?
Ask how many engineers are allocated to your project and whether they’re shared across others. Look for signs of stable teams, not revolving-door staffing.
4. Are They Clear on Process and Communication?
Ask about their delivery rhythm. Do they use Agile or Kanban? Weekly sprints? How are updates shared? Jira? Slack?
You’ll need regular updates, working demos, and a shared understanding of what “done” looks like. If it’s vague now, expect chaos later.
5. Can You Trust Them With Your Data?
You’re probably handing over production data, PII, or internal IP. That means you need clarity on:
Who has access
Where data is stored
Whether there’s encryption in transit and at rest
How IP ownership is structured in the contract
You should also have a signed NDA before you share anything valuable.
6. What Happens After Launch?
AI isn’t set-it-and-forget-it. Ask what happens post-deployment. Do they offer monitoring, performance reviews, and model retraining?
If you notice drift, who handles it? Do they offer SLAs or just bill ad hoc?
7. Are They Straight With You?
This might be the biggest one. A good partner admits tradeoffs, flags risks early, and doesn’t overpromise. If everything sounds too good, dig deeper.
Bottom Line
Choose a team that codes well, communicates clearly, and handles the full lifecycle, not just the prototype. You’re not hiring for potential. You’re hiring for results.
Let them prove it.
Top comments (0)