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Anjum Zaki
Anjum Zaki

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Building Production-Ready AI Demand Forecasting for Retail: Architecture & Code

#ai

Problem Statement
Retail forecasting breaks down when the real world intervenes. Weather, trends, and regional shifts mess with static models. We needed an AI system that could handle structured and unstructured inputs and adapt by region.
System Architecture
class DemandForecastingSystem:
def __init__(self):
self.data_collector = DataCollector()
self.preprocessor = DataPreprocessor()
self.llm_engine = LLMPredictionEngine()
self.api_interface = PredictionAPI()
Data Collection Layer
`class DataCollector:
def init(self):
self.pos = POSConnector()
self.weather = WeatherAPI()
self.social = SocialMediaMonitor()
self.economy = EconomicIndicators()

async def collect(self, store_id, date_range):
    pos = await self.pos.get_sales(store_id, date_range)
    weather = await self.weather.get_forecast(store_id)
    sentiment = await self.social.get_sentiment()
    return self.merge(pos, weather, sentiment)`
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LLM Demand Engine
`class LLMPredictionEngine:
def init(self):
self.model = self.load_model()
self.context_builder = ContextBuilder()

def predict_demand(self, product_id, store_id, prediction_date):
    context = self.context_builder.build(product_id, store_id, prediction_date)
    result = self.model.predict(context)
    return {
        'product_id': product_id,
        'predicted_demand': result.value,
        'confidence': result.confidence,
        'factors': result.factors
    }`
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Real-time API
`from fastapi import FastAPI
from pydantic import BaseModel

app = FastAPI()

class PredictionRequest(BaseModel):
product_id: str
store_id: str
prediction_horizon: int

@app.post("/predict")
async def predict(request: PredictionRequest):
return forecasting_system.predict_demand(
request.product_id,
request.store_id,
request.prediction_horizon
)
Data Preprocessing Pipeline
class DataPreprocessor:
def clean_sales_data(self, raw_data):
cleaned = raw_data.fillna(method='ffill')
q1 = cleaned.quantile(0.25)
q3 = cleaned.quantile(0.75)
iqr = q3 - q1
return cleaned[(cleaned >= (q1 - 1.5 * iqr)) & (cleaned <= (q3 + 1.5 * iqr))]
Monitoring & Drift Detection
class ModelMonitor:
def init(self):
self.threshold = 0.80

def check_accuracy(self, predictions, actuals):
    accuracy = self.calculate_accuracy(predictions, actuals)
    if accuracy < self.threshold:
        self.retrain()

def retrain(self):
    # Trigger retraining pipeline
    pass`
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Deployment Setup
AWS ECS (microservices)
RDS (structured data)
S3 (artifacts and logs)
CloudWatch (monitoring and alerting)
Key Takeaways
85% accuracy from 65%
Real-time predictions <200ms
99.9% uptime
$1.2M savings, 340% ROI
Focus on data quality, gradual rollout, and end-to-end integration.

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