Every software agency claims to do AI now. Most of them are wrapping ChatGPT in a web form and calling it a product. We take a different approach. We aren't above hype. We've just seen what actually works in production, and it is rarely the flashiest demo.
We have shipped AI features into insurance platforms (automated claims triage), pharma compliance systems (MLR reference checking), and contact center analytics (real-time sentiment and compliance scoring). Here is what we have learned.
Start with the workflow, not the model
Every successful AI feature we have shipped started with a workflow problem, not a model capability. The question was never "what can GPT-4 do?" but "what step in this process takes too long and is highly repetitive?" For EventSured, it was claims triage. For Vero, it was checking marketing claims against clinical references. For Voox.ai, it was scoring agent performance on every call.
When you start with the workflow, the model choice becomes an engineering decision, not a product decision. Sometimes the best tool is a simple classifier with 100 lines of Python, not a frontier model.
Boring AI is good AI
The most successful AI features are invisible. Users submit a claim and it gets routed to the right adjuster automatically. They don't think "AI is processing my claim." They think "the system works." We prefer the term "automation" over "AI" in client conversations because it sets the right expectation: this thing reliably does a job that a human used to do.
Reliability matters more than capability. A feature that works 99% of the time with moderate intelligence is infinitely more valuable than one that works 80% of the time with breathtaking intelligence. We spend as much time on error handling, fallback logic, and confidence thresholds as we do on model selection.
Building the loop
The most important AI feature we build is often invisible to end users: the feedback loop. When the system makes a prediction (this claim is urgent, this reference is invalid, this agent needs coaching), we capture whether the human agreed or overrode it. That feedback retrains and improves the model over time.
This is the "learning" in RadicalLoop. The model doesn't get smarter by magic. We deliberately build the data collection and retraining pipeline into every feature. The system compounds over time, getting more accurate with every cycle. That is the only kind of AI we ship.