From Concept to Reality: How 0to60.ai Operationalizes Agentic AI at Enterprise Scale

At 0to60.ai, we don’t just talk about agentic AI. We’ve built a platform that helps financial institutions actually put it to work.

It’s designed for real-world environments, not just prototypes. That means live data, legacy systems, security policies, compliance needs which all are accounted for, from day one.

With our low-code platform, teams can create, manage, and scale intelligent agents that operate independently, coordinate across systems, and deliver results you can measure

Here's what’s under the hood:

Agent Builder
A no-code interface to define agent roles, goals, data inputs, outputs and memory. 

Build once, adjust easily, no code required.

Orchestration Engine
Manages how agents interact, make decisions and complete tasks. Ideal for multi-step processes like credit assessments or fraud resolution.

Enterprise Integration Layer
Connects with platforms like Snowflake, SAP, Oracle and internal banking systems so agents can access live, secure data without disruption.

Feedback and Learning Loops
Agents improve over time. They learn from outcomes, take user feedback and adapt their behavior to become more effective with every cycle.

Everything is governed, auditable and secure, so financial institutions can stay compliant while scaling automation.

This isn’t a future roadmap. It’s already running in production

One of the leading banks in the Middle East had a familiar problem i.e., credit risk reporting was slow, manual and expensive.

Every month, analysts spent over a week pulling data from three different systems, validating figures across regions and stitching together reports in spreadsheets. The process was prone to delays, version mismatches and audit concerns. It wasn’t scalable, and it certainly wasn’t strategic.

The solution: an agent built for risk intelligence

Using the 0to60.ai platform, the bank deployed a Risk Reporting Agent designed to:

• Connect to Snowflake, Oracle credit systems, and regulatory templates
  
• Run daily data extractions and automatic validations
• Detect exposure anomalies and policy breaches
  
• Generate draft reports aligned with compliance standards
  
• Respond to analyst queries like
  “Show all clients with over 20 percent exposure change in the past 14 days by sector”

What changed?

• Reporting time dropped by 85 percent
  
• Audit readiness improved with traceable agent actions
  
• Analysts stopped chasing spreadsheets and started focusing on insights
  
  

The entire rollout took less than six weeks. No custom code. No disruption to core systems.

This wasn’t a pilot. It was production, and it worked.

The impact of agentic AI in banking goes well beyond reporting. At 0to60.ai, we're seeing real results across multiple functions where automation alone used to fall short.

Here are two examples from recent deployments:

Claims Automation Agent – Insurance

One insurer used 0to60.ai to automate medical claims processing. The agent extracts data from submitted forms, validates it against policy rules, and initiates the approval workflow without manual input.

Impact:

• 60% faster processing time
  
• Fewer errors and quicker turn around for customers

Sales Intelligence Agent – B2B Services

A financial services firm implemented an agent to support their sales teams. It analyzes CRM records, meeting notes, and emails to suggest the next best actions and surface at-risk deals.

Impact:

• 25% increase in lead engagement
• Better visibility into pipeline performance and forecasting.

These agents were configured and deployed using the same platform, in under six weeks, without any heavy integration work. They’re modular, scalable, and designed to work within the systems financial institutions already use.