Generic Agent AI Β· Token Efficiency Β· 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
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0.5Γ— ← Perlahan Β· Cepat β†’
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🧠 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
πŸ“₯
Load bank statements100 sets from different SMEs & bank formats
2
πŸ—‚οΈ
Segment by formatGroup by bank type, detect common patterns
3
πŸ”€
Route intelligentlyRules engine (1.2k) β†’ AI (4-9k) β†’ Human (rare)
4
πŸ“Š
Generate statementsOutput 100 financial reports with 87% fewer tokens

04 Block Diagram

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05 System Architecture

Smart routing architecture: Input β†’ Segment β†’ Route β†’ Process β†’ Output. AI hanya untuk kes kompleks.

⚑ Rules Engine

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

πŸ€– Selective AI

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

πŸ‘€ 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
πŸ” AI Agent One-by-One
0 tokens
GPU Energy
0 kWh
COβ‚‚
0 kg
SMEs: 0/100
🧭 Detached + Smart Routing
0 tokens
GPU Energy
0 kWh
COβ‚‚
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 COβ‚‚ 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

πŸ“± Command Layer
WhatsApp instruction
100 SME bank statements
πŸ€– 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.
Bank Β· Recon SME Growth