When This Is the Right Model?
This is built for teams that have already launched something, but know it’s not ready for scale.
It’s the right fit if:
- You have an AI MVP or early product in production
- Adoption is inconsistent or low
- AI outputs are unpredictable or unreliable
- Infrastructure costs are increasing
- Technical debt is accumulating
- You’re unsure whether to rebuild or optimise
If you’re asking, Is this really ready to scale?, this is your checkpoint.
What You Get
You gain clarity before committing to heavy delivery investment.
Deliverables include:
- Product and usability assessment
- AI architecture and pipeline review
- Model, prompt, and evaluation framework analysis
- Scalability and performance review
- Production readiness checklist
- Security, compliance, and data handling review
- 3–6 month productization roadmap
- Build vs. buy recommendations
- Effort, cost, and risk estimation
You’ll know what to fix, what to simplify, and what to scale.
How it works
We evaluate both the product layer and the technical layer. No experiments. No vague advice. Just actionable direction.
Diagnostic assessment across UX, AI, and infrastructure
Identification of failure points and scalability risks
Clear, prioritized roadmap for moving from MVP to production
Timeline & Engagement
AI Product Readiness engagements typically run 3-4 weeks, end to end.
Designed as a focused diagnostic before scaling delivery.
Starting from €12.000
Ready to Make Your AI Product Production-Ready?
Book a focused AI Product Readiness assessment and define the fastest path from MVP to scalable product.
FAQs
AI Product Readiness is a structured assessment designed to move an AI MVP to production by improving reliability, usability, scalability, and cost control.
MVP development focuses on validating demand before building. AI Product Readiness focuses on stabilizing and scaling an already-built MVP or AI product.
An MVP is ready for production when performance is stable, outputs are reliable, infrastructure scales predictably, and monitoring and guardrails are in place.
It involves architecture review, AI model evaluation, infrastructure optimization, usability improvements, and operational readiness planning.
Yes. Cost-control and performance optimization are core parts of the readiness assessment.
This offering is specifically designed for AI-enabled products. Non-AI products may be better suited for Platform Stabilization Services.
Still Not Sure What to Fix First?
If you’re unsure whether to stabilize, rebuild, or scale your AI product, a short expert conversation can help you identify the real bottlenecks, before they become expensive.
Get clarity before you invest more time and budget