Stop treating AI vendors like a magic bullet. They're selling you a tool, not a solution. Your job is to ensure that tool builds *your* production system, on *your* terms, with *your* ownership.
You're about to sign a contract that promises AI transformation. The demo was slick, the pitch was smooth. But before you commit, ask yourself: are you buying a production system or a perpetual dependency? At FACTA, we know the difference. We build production AI systems in 90 days. That means relentless focus on what keeps a system alive: boring infrastructure, owned tooling, and controlled credentials. This isn't about trusting a vendor; it's about verifying they enable *your* control, not just their own.
Vendor diligence for AI isn't just a compliance checkbox; it's a strategic imperative. The market is flooded with "AI solutions," but very few are built for long-term operational reality. Don't get caught in the hype cycle.
Beyond the Brochure: What's Under the Hood?
Forget the marketing deck. Dig into the technical specifics. This isn't about what the vendor *says* they can do, but what they *enable you* to do.
- **Data Ownership and Portability:** Do you own 100% of the data, including any derived models or embeddings? Can you export it, in a usable format, at any time, without penalty? The OECD Due Diligence Guidance for Responsible AI (https://www.aigl.blog/oecd-due-diligence-guidance-for-responsible-ai/) emphasizes the importance of data governance and accountability. If you can't control your data, you don't control your AI.
- **Infrastructure and Deployment:** Are they deploying on *your* cloud, in *your* accounts, with *your* credentials? Or are they abstracting it all away into their black box? Production systems live on infrastructure you own, not leased space.
- **Integration and APIs:** How does their solution integrate with your existing tech stack? Are there robust, well-documented APIs, or are you looking at bespoke connectors and manual processes? "GitHub vs Vercel vs Replit: What Dev Platforms Do When AI Code Is Cheap" (https://blog.bytebytego.com/p/github-vs-vercel-vs-replit-what-dev) highlights the importance of platform extensibility and integration for developers – the same applies to your AI systems.
Operational Reality: Beyond the Demo
A demo is a snapshot; a production system is a marathon. Your diligence needs to focus on the long haul.
- **Observability and Monitoring:** Can you monitor the system's performance, health, and cost in real-time, using your own tools? Do you have access to logs, metrics, and alerts? If you can't see it, you can't fix it.
- **Failover and Disaster Recovery:** What's the plan when things go wrong? Is there a clear, tested failover strategy that *you* control? Don't rely on a vendor's promise; demand the details of their DR plan and how it integrates with yours.
The Ownership Mandate: Your Long-Term Control
This is the core of FACTA's approach: full ownership handoff. Your vendor's solution should empower you, not entangle you.
**Code Access and Escrow:** For proprietary components, is there a code escrow agreement? What happens if the vendor goes bankrupt or changes their business model?
**Model Versioning and Retraining:** Can *you* version your models? Can *you* retrain them with new data without vendor intervention? Your AI system needs to evolve with your business, not be locked into a vendor's release cycle.
**Cost Transparency:** Is the pricing model clear, predictable, and directly tied to your usage, not some opaque "value metric"? Hidden costs kill AI initiatives faster than technical debt.
**Vendor Lock-in Assessment:** How difficult would it be to switch vendors if necessary? Look for open standards, data portability, and well-defined APIs to minimize lock-in. As Gartner® Recognizes Holistic AI as a Representative Vendor in its Market Guide for Guardian Agents (https://www.holisticai.com/blog/gartner-guardian-agent-vendor-guide) implies, even "guardian agents" need to be evaluated for their integration and interoperability within your existing ecosystem.
What to watch
- **Black Box Syndrome:** The vendor can't or won't explain how their AI works, how it was trained, or how to debug it.
- **Proprietary Infrastructure Lock-in:** The solution only runs on their custom cloud, making migration impossible.
- **Vague SLAs and Support:** Promises of "best effort" support or SLAs that don't address critical uptime and performance.
Conclusion
Signing with an AI vendor is a commitment. Ensure that commitment is to your future, not theirs. Demand transparency, control, and clear pathways to ownership. This isn't about trust; it's about verifiable engineering and operational reality.
Sources
- OECD Due Diligence Guidance for Responsible AI (https://www.aigl.blog/oecd-due-diligence-guidance-for-responsible-ai/)
- Gartner® Recognizes Holistic AI as a Representative Vendor in its Market Guide for Guardian Agents (https://www.holisticai.com/blog/gartner-guardian-agent-vendor-guide)
- GitHub vs Vercel vs Replit: What Dev Platforms Do When AI Code Is Cheap (https://blog.bytebytego.com/p/github-vs-vercel-vs-replit-what-dev)
About FACTA
FACTA helps startups and growth-stage teams turn AI into production systems that keep running — not demos that impress once.
We design the architecture around the parts that actually break under real usage: tooling you own, credentials you control, failover, cost controls, observability. The boring infrastructure that keeps a system alive after launch.
Led by Matías Baglieri and Carolina Fogliato, we focus on one thing:
AI leadership that builds. Not just advises.
Ready to build an AI system that you own, control, and can scale? Don't just buy a solution; build a capability.
Let's talk about shipping a production AI system in 90 days. Talk to FACTA
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