Choosing the right AI model is 10% of the work. Connecting it to your systems, training it on your data, and keeping it running is the other 90%. Rendai handles all of it for Tampa businesses.
87% of AI projects never make it to production. The technology works. The implementation does not.
You know AI could help your business. You do not have an AI engineer. Hiring one costs $200K+ and takes 6 months. You are stuck.
AI needs clean data. Your data is in 5 systems, 3 spreadsheets, and someone's email. Before you can use AI, someone has to untangle the mess.
The demo worked great. Then it hit real data, real edge cases, real users. It broke. Nobody fixed it. Now AI is "that thing we tried."
We solve the implementation problem by staying embedded and building continuously.
No 6-month planning phase. First War Room: we identify the single highest-impact AI opportunity. First month: it is live in production.
Every AI system we build handles real data, real edge cases, and real users from day one. We test against your actual operations, not sample data.
12 months = 12 implementations. Not one big project - 12 discrete capabilities that compound. By month 6, your business operates fundamentally differently.
100%
Deployed to production
<30 days
First system live
12
Implementations per year
$2,500
Per month
87% of AI projects never make it to production. The technology works - the implementation does not. Common failures: no one to maintain it after launch, built on demo data instead of real operations, no plan for edge cases. We solve all three by staying embedded.
Within 30 days. No 6-month planning phase. First War Room identifies the highest-impact target. First month, it is live in production running against real data and real operations. We start immediately.
That is normal. Part of our implementation process is connecting to your data wherever it lives and building pipelines to clean and structure it. We do not require a 6-month data cleanup before we start. We work with what you have.
We are model-agnostic - OpenAI, Anthropic, Google, and open-source models depending on the task. We build primarily on AWS but work with any cloud or on-premise setup. We pick the right tool for each job.
It happens - and because we are embedded, we iterate fast. A failed implementation becomes a learning that improves the next attempt. We diagnose why, adjust the approach, and try again in the same cycle. No waiting for another SOW or budget approval.
AI implementation that actually ships - every month in Tampa.