RISK 01
Cost per use is unclear
Model, search, storage, and data costs grow differently and are barely visible in a prototype.
Make AI production-ready
For SaaS teams moving from an AI prototype to production
From a compelling AI demo to a reliable product
A prototype answers whether an idea works. Production must also manage costs, privacy, quality, and failures. The audit shows which infrastructure you genuinely need and which you can avoid.
30 minutes · directly with Niels · no sales script
8+
years of infrastructure experience
12x
AWS certified
Golden
CNCF Kubestronaut
Direct
access to the expert doing the work
Does this sound familiar?
AI rarely fails in production because of the demo
With real users, data volume, load, quality expectations, and liability risks change. That is where costly surprises emerge.
RISK 02
Quality is not measurable
Without reproducible tests, incorrect or outdated answers remain a matter of intuition.
RISK 03
Data flows are risky
Personal or confidential data enters systems whose access and storage arrangements have not been clearly resolved.
After the engagement
A well-founded go/no-go decision for production
We assess the complete path from the data source to the user response and avoid speculative infrastructure. The result may also be that you do not need a vector database.
An appropriate architecture
Components follow the real use case rather than the current AI hype.
Predictable unit costs
The main cost drivers and useful metrics become visible.
Controlled operations
Quality, privacy, failures, and recovery are given clear guardrails.
What you receive
A defined scope instead of open-ended consulting days
- ✓Use case, quality objective, and business-critical errors
- ✓Data sources, updates, permissions, and deletion
- ✓Need for retrieval or a vector database, and alternatives
- ✓Latency, scaling, failure scenarios, and dependencies
- ✓Cost model per request, customer, or feature
- ✓Monitoring, testing, and phased production plan
How it works
Three steps to a clear decision
01
STEP 01
Understand the context
We clarify the objective, environment, and current pressure to act. You then know whether the assessment is genuinely a fit.
02
STEP 02
Assess what matters
I review documentation, configuration, and data and speak with the people responsible for the environment.
03
STEP 03
Make decisions
In the readout, we prioritize findings, effort, and next steps together according to business impact.
This is a fit if ...
- ✓An AI or RAG prototype exists or is specifically planned
- ✓The feature is intended for real customers or core internal processes
- ✓Costs, privacy, or operational viability have not yet been answered reliably
This is not a fit if ...
- ×You need support training your own foundation model
- ×There is no concrete use case or accountable product owner yet
You work directly with Niels
The expert in the first call also carries out the assessment
I am Niels Heidbrink. For more than eight years, I have built and operated cloud infrastructure for SaaS companies, software vendors and e-commerce, on AWS, Azure, IONOS and Hetzner. I hold 12 AWS certifications and the CNCF Golden Kubestronaut title.
“Niels works in an extremely structured, efficient, and solution-oriented way. He thinks beyond the immediate task, understands connections quickly, and implements requirements pragmatically and reliably.”
Frequently asked questions
What you should know before the call
Do we always need a vector database for RAG?+
No. Data volume, search patterns, freshness, and existing systems determine the answer. I recommend the simplest solution that meets the quality and operational objectives.
Do you also assess managed AI services on AWS?+
Yes. Managed services can save development time, but they raise questions about cost and dependency. Both are assessed in the context of your product.
Is this a data protection consultation?+
No. The audit makes technical data flows, access, and storage locations visible. Legal assessment remains with your data protection or legal advisers.
The next step
Let’s find out whether the AI Infrastructure Readiness Audit solves your problem
In a free 30-minute call, we discuss your situation. If this assessment is not the right next step, I will tell you openly.
Book a free fit checkNo preparation required · no obligation
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