0.5×
← Perlahan · Cepat →
🧠
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
Menunggu Sedang aktif Selesai
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
import json, csv
BATCHES = ['maybank.csv','cimb.csv','bni.csv'] # segmented by bank
ROUTES = {'rules': {'tokens': 2000, 'pct': 72},
'ai': {'tokens': 6500, 'pct': 23},
'human': {'tokens': 12000,'pct': 5}}
def route_statement(stmt):
if stmt['format'] in ('standard','known'):
return 'rules' # ~1.2-2.8k tokens
if stmt['anomaly_score'] > 0.7:
return 'human' # ~12k tokens
return 'ai' # ~4-9k tokens
def process_batch(batch):
total = 0
for stmt in batch:
route = route_statement(stmt)
total += ROUTES[route]['tokens']
stmt['route'] = route
return total
all_statements = [s for b in BATCHES for s in csv.DictReader(open(b))]
total = process_batch(all_statements)
print(f"Total tokens: {total} (vs 7.5M baseline)")
print(f"Efficiency: {round((1 - total/7500000)*100)}% saved")
04 Block Diagram
Menunggu Sedang aktif Selesai
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
SMEs: 0/100
🧭 Detached + Smart Routing
0 tokens
SMEs: 0/100
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.