Logic-Layer Decoupling: Updating AI Without a Single Line of Code

Last quarter, a FinTech company had to update their AI system because of a new accounting regulation. The change was simple: update the definition of "EBITDA."

In a traditional system, this took 3 weeks and $15,000 in developer time. Why? Because the rule was buried deep in thousands of lines of Python code.

At OpsSolved, we solve this with Logic-Layer Decoupling.

The Problem: Hardcoded Business Logic

Most AI systems are built with business rules "hardcoded" by developers. This is a disaster for three reasons:

1. Slow: Every change requires a developer, a code review, testing, and a new deployment.
2. Disconnected: Developers aren't Subject Matter Experts (SMEs). They don't always understand the nuance of tax law or medical compliance.
3. Expensive: You shouldn't pay a Senior Engineer to update a definition in a text file.

The OpsSolved Solution: Logic as Data

We separate the Reasoning Engine (the AI) from the Business Logic (your rules). We store your standards, checklists, and definitions in simple YAML or JSON templates.

How it works:

1. Rules in Files: All your compliance rules and business definitions live in a separate directory.
2. SME Ownership: Your experts (financial analysts, lawyers, or doctors) can edit these files directly using plain text. No coding required.
3. Automatic Updates: The AI reads these files every time it runs. When an expert saves a change to a rule, the AI's behavior updates instantly.

Real-World Example: HIPAA Compliance

Imagine a healthcare company using AI to check data handling.

• Old Way: The regulation changes to require "In-Memory Encryption." A developer spends 8 days finding, updating, and re-deploying the code.
• Logic-Layer Decoupling: A compliance officer opens hipaa_rules.yaml, adds one line to the "encryption_requirements" list, and saves. Total time: 5 minutes. Cost: $0.

The Benefits of Separation

1. Scale with Expertise

Your business experts should be in control of business rules. Decoupling gives the power back to the "Adults in the Room"—the people who actually understand the standards.

2. Auditability

Since rules are stored in simple files, we can use version control (Git). You can see exactly who changed a rule, when they changed it, and why. This is critical for DORA and KNF compliance.

3. Reduced Maintenance

By removing the need for developer intervention for every minor update, you slash your long-term maintenance costs and eliminate "technical debt" build-up.

Conclusion

We profit from delivering high-end architecture, not from billing you for minor text changes. Logic-Layer Decoupling is the "Industrial Standard" for flexible, scalable AI.

We build the engine. You drive the rules.

OpsSolved: Engineering sovereignty into your business.