Context Window Limits
LLMs can't handle 500+ contact drivers in context. Accuracy drops below 70% on real-world data.
Our Technology
Not Another GPT Wrapper
Agentic AI that does the work-not just dashboards you have to interpret. Model orchestration delivers 97% accuracy, 30% better than LLMs alone.
The Problem
Why Generic AI Falls Short
GPT wrappers can't handle the complexity of financial services compliance.
No Domain Expertise
General-purpose models don't understand UDAAP, Reg E, or sponsor bank requirements. They hallucinate compliance guidance.
Inconsistent Results
Same input, different output. Not acceptable for compliance decisions that regulators will scrutinize.
Our Approach
Agentic AI Architecture
AI agents that take action-not just surface insights. The right model for the right task.
Layer 01
Ingest & Transcribe
Layer 02
Model Orchestration
Layer 03
Agent Execution
Layer 04
Action & Output
Large Language Models
General reasoning, nuanced interpretation, edge case handling. Used where their strengths matter-not as a one-size-fits-all solution.
Specialized Small Models
Fine-tuned for specific tasks: sentiment, intent classification, complaint detection. 10x faster and more accurate than general LLMs.
Traditional ML
Pattern recognition, anomaly detection, statistical analysis. Proven techniques where they outperform neural approaches.
Your Policies, Encoded
Send us your policies, procedures, and scorecards. Our AI configures to your definitions-not generic industry standards. No more "close enough" detection.
- Your complaint definitions
- Your QA scorecards
- Your compliance criteria
- Configured in days, not months
Example: Complaint Definition
Input: Your 15-page complaint policy PDF Output: AI model fine-tuned to your exact definitions, exceptions, and escalation criteria. No manual rule-writing. No regex patterns to maintain. 97% detection accuracy.
Proven Results
Accuracy That Compounds
Our AI gets smarter from every conversation. Accuracy improves over time-without retraining.
97%
Complaint Detection
vs. 70% industry average
30%
Better Than LLMs
On real financial services data
500+
Contact Drivers
Auto-discovered via clustering
Discovery Engine
Semantic Clustering for Discovery
The Problem
Agents tag calls inconsistently. You don't really know why customers are calling. Manual analysis of call samples gives you guesses, not ground truth.
Our Solution
AI analyzes conversation content and automatically discovers contact drivers. 500+ categories, no manual tagging. You see what's actually happening-not what agents think is happening.