Safe Automation

Why Safe Automation Matters

Automating business processes with AI requires more than just accuracy — it requires predictable, controllable behavior. This is especially critical in accounting and finance, where errors have real consequences.

Aito is designed from the ground up to support safe, gradual automation.

How Aito Differs from LLMs

LLMs generate plausible-sounding answers even when they don't know. Aito's probabilistic approach means it can tell you when it's uncertain — enabling safer automation decisions.

Confidence Thresholds

Every Aito prediction includes a confidence score between 0 and 1. You control what happens at different confidence levels:

Prediction: "Marketing" (confidence: 0.92)
→ Auto-apply: High confidence, process automatically

Prediction: "Marketing" (confidence: 0.65)
→ Human review: Medium confidence, flag for review

Prediction: null (confidence: below threshold)
→ Abstain: Low confidence, route to human

Setting Your Threshold

The right threshold depends on:

• Error cost: Higher cost → higher threshold
• Volume: Higher volume → more value from automation
• Review capacity: Limited reviewers → higher threshold

Start conservative (e.g., 0.85) and adjust based on observed accuracy.

Abstain Behavior

When Aito's confidence falls below your threshold, it abstains — returning no prediction rather than a potentially wrong one. This is fundamentally different from systems that always provide an answer.

Benefits of abstain behavior:

• No silent failures: You know when the system is uncertain
• Measurable automation rate: Track what percentage processes automatically
• Graceful degradation: System handles edge cases by routing to humans

Human-in-the-Loop Workflows

Recommended rollout pattern for accounting automation:

Phase 1: Shadow Mode

• Aito predicts, humans decide
• Compare Aito's predictions to human decisions
• Measure potential automation rate and accuracy

Phase 2: Suggestion Mode

• Aito suggests, humans approve/reject
• Build confidence in edge case handling
• Train team on when to override

Phase 3: Auto-Apply with Review

• High-confidence predictions auto-apply
• Medium-confidence flagged for review
• Low-confidence routed to humans

Phase 4: Full Automation

• Automated processing within thresholds
• Exception handling for abstains
• Periodic accuracy audits

Audit Trail

For compliance and accountability:

• Log all predictions: Store prediction, confidence, and explanation
• Track overrides: Record when humans disagree with Aito
• Version your data: Know what training data produced each prediction

Aito provides prediction explanations via the API, showing which fields influenced each decision.

Example: GL Account Coding

A typical accounting automation workflow:

1. Invoice arrives with vendor, amount, description
2. Aito predicts GL account with confidence score
3. Routing logic:
   • Confidence ≥ 0.90: Auto-code
   • Confidence 0.70-0.89: Queue for review
   • Confidence < 0.70: Manual coding
4. Feedback loop: Corrections improve future predictions

Example: VAT Classification

Input: {
  "vendor_country": "DE",
  "product_type": "Software License",
  "buyer_country": "FI"
}

Prediction: {
  "vat_treatment": "Reverse charge",
  "confidence": 0.94,
  "explanation": {
    "vendor_country": 0.35,
    "product_type": 0.45,
    "buyer_country": 0.20
  }
}

The explanation shows that product type and vendor country were the strongest factors — information a reviewer can quickly verify.

Measuring Automation Success

Key metrics to track:

A healthy automation setup shows high automation rate, high accuracy, reasonable abstain rate, and low override rate.

Questions?

For guidance on implementing safe automation for your specific use case, please contact us.