AI-Safe Systems • Operations • Validation

AI Operations Design & Validation

I help product and engineering teams integrate AI into operations-heavy systems without risking core business logic.

This service focuses on architecture, rules, and validation — not chatbots, hype, or opaque automation.

The Problem

Many teams add AI directly into pricing, planning, or operational workflows. This creates systems that are difficult to test, impossible to audit, and unsafe to scale.

AI should never be the source of truth for costs, budgets, or risk.

How Risky Is Your AI Integration?

5-Minute Risk Assessment

Answer a few questions to understand your AI integration risk level and get personalized recommendations.

—

Risk Level

Complete assessment above

Recommendation

Next Step:

The Approach

Deterministic First

Explicit rules handle costs, constraints, thresholds, and risk detection. These outputs are testable and explainable.

AI as Advisor

AI receives validated system outputs and provides suggestions — never modifying system facts.

Reference Validation

A Python reference implementation confirms system behavior before production integration.

Want to see examples of AI-safe architectures? Download our free integration guide →

What I Deliver

See Validation in Action

Working Python Validation Test Suite

Hover over any line to see what it does. This is what deterministic validation looks like.

import pytest
from decimal import Decimal
 
class PricingEngine:
    def __init__(self, base_rate, discount_threshold):
        self.base_rate = Decimal(str(base_rate))
        self.discount_threshold = Decimal(str(discount_threshold))
 
    def calculate_price(self, quantity, unit_cost):
        if quantity <= 0 or unit_cost <= 0:
            raise ValueError("Quantity and cost must be positive")
 
        total = Decimal(str(quantity)) * Decimal(str(unit_cost))
        if total >= self.discount_threshold:
            total *= Decimal("0.9") # 10% discount
 
        return float(total)
 
class TestPricingEngine:
    def setup_method(self):
        self.engine = PricingEngine(base_rate=50.0, discount_threshold=1000.0)
 
    def test_basic_calculation(self):
        result = self.engine.calculate_price(10, 50.0)
        assert result == 500.0, "Basic calculation failed"
 
    def test_discount_applied(self):
        result = self.engine.calculate_price(25, 50.0)
        assert result == 1125.0, "Discount applied correctly"
 
    def test_edge_cases(self):
        with pytest.raises(ValueError):
            self.engine.calculate_price(-1, 50.0)

Who This Is For

Engagement Model

Architecture Review

$3,500
One-time engagement
  • System audit and risk assessment
  • AI integration strategy document
  • Deterministic boundary definition
  • Implementation recommendations
  • 2 review sessions

Design & Validation

$8,500
2-3 week engagement
  • Everything in Architecture Review
  • Detailed system design documentation
  • Rules and parameter modeling
  • Python reference implementation
  • Validation test suite
  • Team handoff session

Advisory Retainer

$4,000/mo
Ongoing support
  • Monthly architecture reviews
  • Integration planning and support
  • Validation strategy updates
  • Team technical guidance
  • Async support via Slack/email

Free Resources

AI Integration Safety Checklist

A 12-point checklist for evaluating AI integration risk before you build. Includes architecture patterns, validation strategies, and common failure modes.

✓ Checklist sent! Check your inbox.

Reference Architecture Template

A Python template showing deterministic-first design with AI as an advisor layer. Includes example rules engine, validation logic, and test cases.

✓ Template sent! Check your inbox.

Let's Discuss Your Project

Tell me about your AI integration challenge and I'll reach out with specific recommendations.

✓ Thanks! I'll review your project and reach out within 24 hours.

Want to Add AI Without Breaking Your System?

Let's review your product and design a safe, testable integration strategy.

Contact AtharUX