Operating at millions-of-customers scale with rapid growth, the company was seeing outsourced compliance services as a rapidly growing cost-center. Manual verification created bottlenecks, inconsistent quality, slow processing times, and limited their ability to scale customer acquisition. More critically, they had no control over the process, they were outsourcing a compliance-critical function. The urgency was operational: they needed to bring verification in-house, cut costs, and prove they could scale AI automation without requiring months of manual prompt engineering for every use case.

This initiative is a mix of cost cutting, process efficiency, and top-line impact. The faster clients can be onboarded, the faster cash flow is realized.

The Challenge

Automate address verification for customer onboarding across multiple European markets. This leading digital broker needed to replace their BPO-dependent manual verification process with an AI system that could validate customer documents against country-specific regulatory requirements at scale.

The Options

The broker could have:

• Continued paying BPO providers indefinitely with no process control.

• Spend internal resources on prompt engineering specialists to manually tune AI systems over months.

• Built traditional rule-based automation that that wouldn't be able to achieve a close to 100% processing rate due to the inflexibility of rule based automation.

All options were slow, expensive, or brittle. Beam offered a fourth path: self-learning AI that optimizes itself in minutes instead of months.

The Solution Deployed

AI agent solution with Auto-Tuner self-learning technology, deployed at production scale for 15,000+ tasks. Full production deployment replacing a core operational process (BPO verification).

Here's what happened

Beam started with a baseline AI agent (60.6% accuracy) embedded with country-specific validation logic.

Then the Auto-Tuner was implemented. It ran an automated feedback loop: compare AI decisions to human expert judgments → identify where the AI failed → analyze the failure patterns → rewrite the prompts automatically → test the new version → repeat. All without human intervention.

In 25 minutes and 3 iterations, the Auto-Tuner drove accuracy from 63.3% to 97-99%% on training examples, and from 60.6% to 95.7% on real validation documents.

The system learned country-specific edge cases, regulatory nuances, and document variations that would have taken prompt engineers months to manually discover and code.

The Results

• 95.7% accuracy on customer documents (up from 60.6% baseline) - a 35.1 percentage point improvement

• 25 minutes to optimize (vs. months of manual prompt engineering)

• 15,000+ verifications processed in production

• 97-99%% accuracy achieved on training examples after just 3 iterations

The bank attained a production-ready AI agent integrated into their onboarding workflow, processing documents in real-time with accept/reject decisions. But more importantly, they unlocked industrial-scale AI deployment capability that can optimize hundreds of processes simultaneously without hiring an army of AI specialists. The Auto-Tuner becomes their AI optimization engine for every compliance process they want to automate.

Summary

The Auto-Tuner is the breakthrough. Most AI implementations fail because they require extensive prompt engineers spending months manually tuning a static system. Beam's Auto-Tuner eliminates that bottleneck entirely, it's an automated prompt optimization engine that makes AI systems learn and improve themselves based on human feedback. With that we are mimicking the human learning curve.