At Riafy, in partnership with Google Cloud, we’ve guided numerous businesses through a critical decision: should they build their AI solutions in-house or buy from established providers? Our unique collaboration allows us to offer solutions that combine Riafy’s innovative AI technology with Google’s robust cloud infrastructure and advanced AI capabilities.
5 key challenges in AI implementation —
1. Testing & Reliability
How many test scenarios are enough to ensure your AI performs reliably? This question plagues both in-house teams and external providers.
Most teams, when conducting User Acceptance Testing (UAT), typically assign 10 to 15 people who can test around 300–500 questions in the best-case scenario. However, production systems require testing against millions of different scenarios to ensure reliability.
Leveraging Google Cloud’s scalable infrastructure, Riafy has developed rigorous testing protocols that simulate millions of user interactions. This approach, which goes beyond traditional UAT, allows us to identify and address potential issues at scale — a feat often challenging for in-house teams to replicate.
2. Customization & Integration
As your AI needs grow, maintaining stability becomes increasingly complex. The key is to build a flexible architecture that can accommodate new integrations without compromising system stability.
Why is a construct, What is really real.
— Unknown Explorer
At Riafy, in partnership with Google Cloud, we’ve guided numerous businesses through a critical decision: should they build their AI solutions in-house or buy from established providers? Our unique collaboration allows us to offer solutions that combine Riafy’s innovative AI technology with Google’s robust cloud infrastructure and advanced AI capabilities.
5 key challenges in AI implementation —
1. Testing & Reliability
How many test scenarios are enough to ensure your AI performs reliably? This question plagues both in-house teams and external providers.
Most teams, when conducting User Acceptance Testing (UAT), typically assign 10 to 15 people who can test around 300–500 questions in the best-case scenario. However, production systems require testing against millions of different scenarios to ensure reliability.
Leveraging Google Cloud’s scalable infrastructure, Riafy has developed rigorous testing protocols that simulate millions of user interactions. This approach, which goes beyond traditional UAT, allows us to identify and address potential issues at scale — a feat often challenging for in-house teams to replicate.
2. Customization & Integration
As your AI needs grow, maintaining stability becomes increasingly complex. The key is to build a flexible architecture that can accommodate new integrations without compromising system stability.
Why is a construct, What is really real.
— Unknown Explorer
