OpenClaw · Token Efficiency · Demo

Token Saving in SME Financial Statement Automation

Comparing repeated AI Agent vs Detached System with Smart Routing for 100 SME bank statement sets.

100 SMEs Approach 1 vs 2 Smart Routing 87% Reduction
0.5× ← Perlahan · Cepat →
0% Stage 0 / 7 · Idle
🧠 Tekan Play Demo untuk lihat bagaimana smart routing jimat 87% token berbanding AI ulang 100 kali.
Demo Complete — 87% Token Efficiency Achieved

01 WhatsApp Command

02 Requirement Analysis

Mode
Token Efficiency Study
SMEs
100
Approach 1
7.5M Token
Approach 2
1.0M Token
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03 Python Script Generation

1
📥
Load bank statements100 sets from different SMEs & bank formats
2
🗂️
Segment by formatGroup by bank type, detect common patterns
3
🔀
Route intelligentlyRules engine (1.2k) → AI (4-9k) → Human (rare)
4
📊
Generate statementsOutput 100 financial reports with 87% fewer tokens

04 Block Diagram

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05 System Architecture

Smart routing architecture: Input → Segment → Route → Process → Output. AI hanya untuk kes kompleks.

⚡ Rules Engine

1.2k–2.8k tokens
Standard transactions, known bank formats — 72% of all SMEs

🤖 Selective AI

4k–9k tokens
Complex patterns, ambiguous entries — 23% of SMEs

👤 Human Review

~12k tokens (rare)
High-risk or exceptions — 5% of SMEs
⚠️ Rules engine mesti dikemaskini secara berkala untuk bank formats baru. AI fallback handle sisanya.

06 Live Token Comparison

Mula Simulasi
🔁 AI Agent One-by-One
0 tokens
GPU Energy
0 kWh
CO₂
0 kg
SMEs: 0/100
🧭 Detached + Smart Routing
0 tokens
GPU Energy
0 kWh
CO₂
0 kg
SMEs: 0/100

07 System Report

100
SMEs Processed
6.5M
Tokens Saved (87%)
$5,200
API Cost Saved
47.7
kWh GPU Energy Saved
20.8
kg CO₂ Avoided
87%
Efficiency Rating
The smartest AI system is not the one that uses the most tokens.
It is the one that knows precisely when — and how — to use them.

Terminal Log

[SISTEM] AINNA Token Orchestrator sedia

System Architecture

📱 Command Layer
WhatsApp instruction
100 SME bank statements
🤖 Agent Layer
Statement segmentation
Smart routing logic
Token optimization
Execution Layer
Rules engine (72%)
Selective AI (23%)
Human review (5%)

Safety Notes

  • Rules engine perlu dikemaskini untuk bank formats baru secara berkala.
  • AI fallback handle kes yang rules tak dapat proses — jangan skip.
  • Human review wajib untuk transaction high-risk atau anomaly >0.7.
  • Token counts adalah estimate kasar — actual bergantung pada model dan prompt.