Your Production AI App Checklist
15 Questions Every Organization Should Answer Before Hiring an AI Developer
Empowering your business through excellence in data.
Building an AI system is not like building a typical web or mobile app.
AI projects touch data pipelines, user workflows, compliance, UX, reliability, and—above all—uncertainty.
Before you hire any team, vendor, or consultant, you should be able to answer these foundational questions. This checklist helps you clarify your internal goals, avoid costly missteps, and dramatically increase the chances that your AI project succeeds.
If you can answer these clearly, you’re already ahead of 90% of teams starting an AI initiative.
📌 1. What exact problem are you trying to solve?
AI is not a magic wand.
Be specific:
- What is the pain point?
- Who experiences it?
- How is it currently handled?
- What is the cost of doing nothing?
📝 2. What is the desired output?
AI systems produce outputs — but what form must the output take?
- JSON?
- A dashboard?
- A document?
- A prediction?
- A classification?
- A workflow action?
Clarity here drives architecture.
📂 3. What data will the AI system receive?
A common early-stage trap is underestimating data complexity. Here’s how to avoid it:
- Is the data structured or unstructured?
- How consistent is it across departments or sources?
- Do you have real historical samples?
- Are there outliers or edge cases?
🔍 4. Have you assembled a representative dataset?
Perfect curated examples ≠ real-world data.
Your developer must see:
- messy examples
- historical variation
- formatting inconsistencies
- edge cases
- multilingual inputs
- partial or missing fields
This prevents “demo-only” solutions.
🎯 5. What accuracy do you need? And what accuracy is acceptable early on?
A POC may achieve ~60–80%.
A production model may achieve 90–98%.
But:
Accuracy expectations must match business goals and data reality.
Define your thresholds now.
🧭 6. What does “MVP” mean to you?
-
✅ A. Showcase / Functional Demo MVP
A near-production demonstration designed for:
- stakeholder presentations
- investor conversations
- early workflow exploration
It’s functional and impressive, but intentionally limited. Ideal when you need something demonstration-ready with solid underlying tech, but not yet operational.
-
✅ B. Limited-Use MVP (Internal Pilot)
A more robust version intended for:
- internal testing
- controlled pilot deployments
- early operational workflows
It handles real data with basic reliability, but still isn’t production-grade.
These two MVP paths differ in scope, cost, reliability, and integration depth. Clarifying which one you need upfront ensures accurate scoping, predictable timelines, and a smooth path toward production.
🔗 7. Does the system need to integrate with other tools or platforms?
Integrations often exceed the cost of the AI model itself.
List potential integrations:
- CRMs
- ERPs
- SharePoint
- Slack/Teams
- cloud storage
- proprietary systems
⚖️ 8. What is your tolerance for errors, edge cases, or “unexpected outputs”?
AI systems are probabilistic, not deterministic.
Define:
- what must never go wrong
- what can go wrong occasionally
- what can go wrong rarely
- what triggers human review
This sets expectations for safety and reliability.
🔐 9. What regulatory, compliance, or security requirements apply?
This includes:
- SOC2
- HIPAA
- GDPR
- PCI
- internal infosec standards
- vendor risk assessments
Compliance significantly affects architecture and scope.
👥 10. Who will use the system day to day?
User identity defines functionality.
- staff
- customers
- analysts
- executives
- engineers
Different users = different design constraints.
📈 11. What scale do you expect in the next 6–18 months?
Define expected volume:
- data size
- requests/day
- concurrency
- response time needs
- expected growth
Scaling requires architectural planning early.
🔄 12. How often will the model need to be updated or retrained?
AI system requirements often evolve over time.
You need a plan for:
- retraining
- versioning
- evaluation
- prompt updates
- dataset expansion
💵 13. What is your budget for POC → MVP → Production?
A good developer can shape scope to fit budget, but:
AI must be built in stages: POC → MVP → Production.
Knowing your budget range helps prioritize features.
🧑💼 14. Who on your team will own the project?
Successful AI projects always have an internal owner:
- product manager
- domain expert
- data lead
- internal tester
- stakeholder with decision authority
Without ownership, timelines drift.
⏳ 15. What is your timeline — and is it fixed or flexible?
AI development includes uncertainty.
Define:
- ideal timeline
- acceptable timeline
- absolute deadline
- dependencies (launches, events, investor meetings)
➡️ Ready to Move Forward?
Once you’re confident in your answers, you’re ready to:
- request proposals
- compare vendors
- prioritize features
- begin the POC
- build a roadmap with your chosen developer
This checklist ensures your AI project starts on strong foundations and avoids costly surprises.
Disclaimer: The information provided herein is illustrative and does not create any contractual obligations or guarantees. Specific capabilities, timelines, and deliverables are determined only through a formal engagement, including detailed scoping, data review, and written agreements.
