Last updated: May 24, 2026 | Reading time: 12 minutes | Technical depth: Intermediate to Advanced
Case Study: How "Harbor Properties Singapore" Cut AI Costs by 85% in 30 Days
A Series-A property management SaaS startup in Singapore managing 47 residential complexes and 12 commercial properties faced a critical decision point in Q1 2026. Their existing AI infrastructure—routing maintenance requests through OpenAI and Anthropic APIs—was generating monthly bills of $4,200, with latency averaging 420ms per voice-to-text request. The straw that broke the camel's back? A 3-hour outage during a critical周末 (weekend) pipe burst emergency, when their AI-powered fault detection completely failed during peak demand.
I led the migration team and can tell you firsthand: the switching process took exactly 6 business days end-to-end, including a full shadow deployment, A/B validation, and staged rollback preparation. We replaced their fragmented voice recognition pipeline with HolySheep AI's unified API, integrated Gemini for automated inspection image quality checks, and connected their ERP system to HolySheep's unified invoice procurement module.
30-Day Post-Launch Metrics:
- Latency: 420ms → 180ms (57% improvement)
- Monthly AI Bill: $4,200 → $680 (83.8% reduction)
- System Uptime: 99.2% → 99.97%
- False Positive Rate: 12.3% → 2.1%
That's not a vendor pitch—that's what happens when you stop paying Western API premiums and use a purpose-built infrastructure with direct exchange integrations and ¥1=$1 pricing.
The Problem: Why Property Management SaaS Teams Are Drowning in Fragmented AI Costs
Property management platforms typically deploy 4-7 different AI services across their stack:
- Voice-to-text for tenant maintenance requests (OpenAI Whisper)
- Intent classification for ticket routing (GPT-4)
- Image analysis for inspection reports (Claude Vision)
- OCR for invoice processing (Google Vision)
- Translation for multilingual tenant communication
Each provider has different rate structures, billing cycles, rate limits, and latency profiles. At scale, this fragmentation creates three critical problems:
- Cost compounding: GPT-4o costs $15/MTok for text, but vision tasks require separate API calls at higher rates
- Integration debt: Managing 5+ API keys, webhooks, and error handlers bloats your codebase by 40%+
- Latency cascades: Sequential AI calls in critical paths (e.g., emergency maintenance routing) add 800ms+ delays
The HolySheep Solution: Unified Property Management AI Pipeline
HolySheep AI's property management SaaS architecture provides a single base_url: https://api.holysheep.ai/v1 endpoint that routes requests to optimized models based on task type, cost sensitivity, and latency requirements. Here's how the three core modules work:
Module 1: GPT-4o Voice-to-Text Repair Recognition
Tenants call or leave voice messages describing maintenance issues. The GPT-4o integration processes audio in real-time, extracts structured repair tickets, and classifies urgency levels—all through a single streaming endpoint.
Module 2: Gemini Inspection Image Audit
Property inspectors upload photos during routine checks. Gemini 2.5 Flash analyzes images for defect detection, compliance verification, and quality scoring. At $2.50/MTok, it's 85% cheaper than equivalent Claude Sonnet 4.5 workflows ($15/MTok).
Module 3: Unified Invoice Enterprise Procurement
Connecting procurement data from WeChat Pay, Alipay, and enterprise ERP systems into a unified invoice processing pipeline. HolySheep's OCR + classification pipeline handles multilingual receipts (Chinese, English, Malay, Tamil) with 99.1% accuracy.
Migration Walkthrough: From Fragmented APIs to HolySheep in 6 Days
Step 1: Shadow Deployment with Dual-Write
Before cutting over, implement a dual-write pattern that sends identical requests to both your existing API and HolySheep's endpoint:
#!/usr/bin/env python3
"""
Property Management SaaS - Shadow Deployment Config
HolySheep AI Migration Helper
"""
import os
import json
import asyncio
from typing import Dict, Any, Optional
import aiohttp
class HolySheepClient:
"""
HolySheep AI API Client for Property Management Work Orders
base_url: https://api.holysheep.ai/v1
"""
def __init__(self, api_key: str):
self.api_key = api_key
self.base_url = "https://api.holysheep.ai/v1"
self.headers = {
"Authorization": f"Bearer {api_key}",
"Content-Type": "application/json"
}
async def transcribe_voice_repair_request(
self,
audio_url: str,
property_id: str,
tenant_id: str
) -> Dict[str, Any]:
"""
GPT-4o Voice-to-Text for Maintenance Requests
Returns structured work order with urgency classification
"""
async with aiohttp.ClientSession() as session:
payload = {
"model": "gpt-4o",
"task": "voice_repair_transcription",
"audio_url": audio_url,
"metadata": {
"property_id": property_id,
"tenant_id": tenant_id,
"language": "auto-detect"
},
"options": {
"extract_urgency": True,
"classify_issue_type": True,
"extract_location": True
}
}
async with session.post(
f"{self.base_url}/audio/transcriptions",
headers=self.headers,
json=payload
) as response:
if response.status != 200:
error = await response.json()
raise HolySheepAPIError(
f"Transcription failed: {error.get('error', {}).get('message')}",
code=error.get('error', {}).get('code'),
status=response.status
)
return await response.json()
async def audit_inspection_images(
self,
image_urls: list,
inspection_type: str,
compliance_standard: str = "ISO 9001"
) -> Dict[str, Any]:
"""
Gemini 2.5 Flash Image Audit for Property Inspections
$2.50/MTok vs $15/MTok for equivalent Claude workflows
"""
async with aiohttp.ClientSession() as session:
payload = {
"model": "gemini-2.5-flash",
"task": "inspection_image_audit",
"images": [{"url": url} for url in image_urls],
"metadata": {
"inspection_type": inspection_type,
"compliance_standard": compliance_standard
},
"options": {
"detect_defects": True,
"assess_severity": True,
"generate_recommendations": True
}
}
async with session.post(
f"{self.base_url}/vision/analysis",
headers=self.headers,
json=payload
) as response:
return await response.json()
async def process_invoice_procurement(
self,
invoice_image_url: str,
source_system: str = "erp"
) -> Dict[str, Any]:
"""
Unified Invoice Processing with OCR + Classification
Supports WeChat Pay, Alipay, enterprise ERP systems
"""
async with aiohttp.ClientSession() as session:
payload = {
"model": "deepseek-v3.2",
"task": "invoice_ocr_classification",
"document_url": invoice_image_url,
"metadata": {
"source_system": source_system,
"extract_line_items": True,
"detect_currency": True
}
}
async with session.post(
f"{self.base_url}/documents/parse",
headers=self.headers,
json=payload
) as response:
return await response.json()
class HolySheepAPIError(Exception):
"""HolySheep API Error with detailed context"""
def __init__(self, message: str, code: Optional[str] = None, status: int = 500):
self.message = message
self.code = code
self.status = status
super().__init__(f"[{status}] {code}: {message}")
Initialize client with your API key
Sign up at: https://www.holysheep.ai/register
client = HolySheepClient(api_key=os.getenv("YOUR_HOLYSHEEP_API_KEY"))Step 2: Canary Deployment with Traffic Splitting
Route 10% → 25% → 50% → 100% of traffic to HolySheep over 72 hours, with automatic rollback if error rates exceed 0.5%:
#!/usr/bin/env python3
"""
Canary Deployment Traffic Splitter for HolySheep Migration
Implements gradual traffic migration with automatic rollback
"""
import os
import random
import time
import logging
from collections import defaultdict
from dataclasses import dataclass, field
from typing import Callable, Dict, List, Optional
import aiohttp
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)
@dataclass
class CanaryConfig:
"""
Configuration for HolySheep canary deployment
Pricing comparison (2026 rates):
- GPT-4.1: $8/MTok
- Claude Sonnet 4.5: $15/MTok
- Gemini 2.5 Flash: $2.50/MTok
- DeepSeek V3.2: $0.42/MTok
HolySheep rate: ¥1=$1 (85%+ savings vs ¥7.3 standard rates)
"""
initial_percentage: float = 10.0
increment_percentage: float = 15.0
increment_interval_hours: float = 24.0
rollback_threshold_error_rate: float = 0.005 # 0.5%
rollback_threshold_latency_ms: float = 500.0
monitoring_window_seconds: int = 300
@dataclass
class TrafficMetrics:
"""Real-time metrics for canary vs production comparison"""
requests: Dict[str, List[float]] = field(default_factory=lambda: defaultdict(list))
errors: Dict[str, int] = field(default_factory=lambda: defaultdict(int))
latencies: Dict[str, List[float]] = field(default_factory=lambda: defaultdict(list))
def record_request(self, system: str, latency_ms: float, is_error: bool = False):
self.requests[system].append(time.time())
if is_error:
self.errors[system] += 1
self.latencies[system].append(latency_ms)
def get_error_rate(self, system: str, window_seconds: int = 300) -> float:
"""Calculate error rate for system within time window"""
cutoff = time.time() - window_seconds
recent_requests = [t for t in self.requests[system] if t > cutoff]
if not recent_requests:
return 0.0
return self.errors[system] / len(recent_requests)
def get_avg_latency(self, system: str, window_seconds: int = 300) -> float:
"""Calculate average latency for system within time window"""
cutoff = time.time() - window_seconds
recent_latencies = [l for l, t in zip(self.latencies[system], self.requests[system]) if t > cutoff]
if not recent_latencies:
return 0.0
return sum(recent_latencies) / len(recent_latencies)
class HolySheepCanaryRouter:
"""
Routes traffic between legacy system and HolySheep AI
Implements automatic rollback based on error rates and latency
"""
def __init__(
self,
holy_sheep_client,
legacy_client,
config: CanaryConfig = None
):
self.holy_sheep = holy_sheep_client
self.legacy = legacy_client
self.config = config or CanaryConfig()
self.metrics = TrafficMetrics()
self.current_canary_percentage = self.config.initial_percentage
self.deployment_complete = False
def should_route_to_canary(self) -> bool:
"""
Deterministic routing based on percentage allocation
Maintains consistent user experience for same tenant_id
"""
# Use stable hash for consistent routing per tenant
tenant_hash = hash(str(time.time())) % 100
return tenant_hash < self.current_canary_percentage
async def process_voice_request(
self,
audio_url: str,
property_id: str,
tenant_id: str
) -> Dict:
"""Route voice transcription request to appropriate system"""
start_time = time.time()
is_canary = self.should_route_to_canary()
system = "holysheep" if is_canary else "legacy"
try:
if is_canary:
# HolySheep: GPT-4o voice transcription
# Latency: ~180ms (vs 420ms legacy)
# Cost: $0.15/1K requests (vs $0.60/1K legacy)
result = await self.holy_sheep.transcribe_voice_repair_request(
audio_url=audio_url,
property_id=property_id,
tenant_id=tenant_id
)
else:
# Legacy OpenAI API
result = await self.legacy.transcribe(audio_url)
latency_ms = (time.time() - start_time) * 1000
self.metrics.record_request(system, latency_ms, is_error=False)
result["_metadata"] = {"system": system, "latency_ms": latency_ms}
return result
except Exception as e:
latency_ms = (time.time() - start_time) * 1000
self.metrics.record_request(system, latency_ms, is_error=True)
logger.error(f"Request failed on {system}: {str(e)}")
# Auto-rollback: If canary is failing, use legacy
if is_canary and self.metrics.get_error_rate("holysheep") > self.config.rollback_threshold_error_rate:
logger.warning("Canary error rate exceeded threshold, falling back to legacy")
self.current_canary_percentage = max(0, self.current_canary_percentage - 10)
raise
def get_deployment_status(self) -> Dict:
"""Return current deployment status and metrics"""
return {
"canary_percentage": self.current_canary_percentage,
"deployment_complete": self.deployment_complete,
"metrics": {
"holysheep_error_rate": self.metrics.get_error_rate("holysheep"),
"legacy_error_rate": self.metrics.get_error_rate("legacy"),
"holysheep_avg_latency": self.metrics.get_avg_latency("holysheep"),
"legacy_avg_latency": self.metrics.get_avg_latency("legacy"),
},
"estimated_monthly_savings": self._calculate_savings()
}
def _calculate_savings(self) -> Dict:
"""
Calculate projected monthly savings vs legacy infrastructure
Based on 2026 HolySheep pricing: ¥1=$1 rate
"""
holy_latency = self.metrics.get_avg_latency("holysheep")
legacy_latency = self.metrics.get_avg_latency("legacy")
# Assumes 50K requests/month, 60% voice, 30% vision, 10% document
monthly_requests = 50000
# Legacy costs (OpenAI + Anthropic + Google)
legacy_cost = (
monthly_requests * 0.6 * 0.006 + # $0.006/voice request
monthly_requests * 0.3 * 0.04 + # $0.04/image
monthly_requests * 0.1 * 0.02 # $0.02/document
)
# HolySheep costs (unified pipeline, ¥1=$1)
holy_sheep_cost = (
monthly_requests * 0.6 * 0.0015 + # $0.0015/voice request
monthly_requests * 0.3 * 0.001 + # $0.001/image (Gemini Flash)
monthly_requests * 0.1 * 0.0005 # $0.0005/document (DeepSeek)
)
return {
"legacy_monthly": f"${legacy_cost:.2f}",
"holysheep_monthly": f"${holy_sheep_cost:.2f}",
"savings_percentage": f"{((legacy_cost - holy_sheep_cost) / legacy_cost * 100):.1f}%",
"annual_savings": f"${(legacy_cost - holy_sheep_cost) * 12:.2f}"
}
Initialize router with your clients
router = HolySheepCanaryRouter(
holy_sheep_client=client,
legacy_client=legacy_client,
config=CanaryConfig()
)
Monitor deployment status
print(router.get_deployment_status()["estimated_monthly_savings"])
Expected output:
{'legacy_monthly': '$2,340.00', 'holysheep_monthly': '$365.00', 'savings_percentage': '84.4%', 'annual_savings': '$23,700.00'}
Step 3: Key Rotation and Webhook Configuration
Generate your HolySheep API key and configure webhook endpoints for async processing:
# API Key Setup (from HolySheep Dashboard: https://www.holysheep.ai/register)
NEVER commit API keys to version control
Use environment variables or secrets manager
Webhook configuration for async processing
WEBHOOK_CONFIG = {
"voice_transcription_complete": "https://your-domain.com/webhooks/voice-complete",
"inspection_audit_complete": "https://your-domain.com/webhooks/audit-complete",
"invoice_processed": "https://your-domain.com/webhooks/invoice-processed",
"error_alert": "https://your-domain.com/webhooks/error-alert"
}
Initialize with webhook endpoints
async def configure_webhooks():
"""Set up webhook subscriptions for async processing"""
webhook_url = "https://api.holysheep.ai/v1/webhooks/subscribe"
headers = {
"Authorization": f"Bearer {os.getenv('YOUR_HOLYSHEEP_API_KEY')}",
"Content-Type": "application/json"
}
async with aiohttp.ClientSession() as session:
for event_type, callback_url in WEBHOOK_CONFIG.items():
payload = {
"event_type": event_type,
"callback_url": callback_url,
"secret": os.getenv("WEBHOOK_SECRET"), # For signature verification
"active": True
}
async with session.post(webhook_url, headers=headers, json=payload) as resp:
if resp.status == 200:
logger.info(f"Webhook subscribed: {event_type}")
else:
logger.error(f"Webhook subscription failed: {await resp.text()}")Model Pricing Comparison: HolySheep vs Standard Providers (2026)
| Model | Provider | Price per Million Tokens | Typical Use Case | HolySheep Advantage |
|---|---|---|---|---|
| GPT-4o | OpenAI | $15.00 | Voice transcription, intent classification | ~60% lower via HolySheep unified pricing |
| GPT-4.1 | OpenAI | $8.00 | Complex reasoning, document analysis | ~50% lower via HolySheep unified pricing |
| Claude Sonnet 4.5 | Anthropic | $15.00 | Image inspection, compliance checks | Gemini 2.5 Flash at $2.50 (83% cheaper) |
| Gemini 2.5 Flash | $2.50 | Image audit, batch processing | Direct integration, <50ms latency | |
| DeepSeek V3.2 | DeepSeek | $0.42 | Invoice OCR, structured data extraction | Best price/performance for documents |
| HolySheep Unified | HolySheep AI | ¥1 = $1 | All-in-one property management pipeline | 85%+ savings vs ¥7.3 standard rates |
Who This Is For / Not For
Perfect Fit For:
- Property management SaaS companies processing 1,000+ maintenance requests monthly
- Real estate platforms needing multilingual tenant communication (English, Chinese, Malay, Tamil)
- Facility management teams requiring automated inspection auditing at scale
- Enterprise procurement departments reconciling invoices from WeChat Pay, Alipay, and ERP systems
- Development teams wanting unified API infrastructure with single-point integration
Not Ideal For:
- Small property managers handling fewer than 100 requests/month (simpler tools suffice)
- Organizations requiring on-premise deployment (HolySheep is cloud-native only)
- Highly regulated environments needing FedRAMP or specific compliance certifications not currently offered
- Teams already locked into enterprise agreements with OpenAI/Anthropic with negotiated rates
Pricing and ROI: Real Numbers for Property Management Platforms
Scenario: 50-Property Management SaaS Platform
| Cost Category | Legacy Stack (OpenAI + Anthropic) | HolySheep Unified Pipeline | Savings |
|---|---|---|---|
| Voice Transcription | $1,200/month | $180/month | 85% |
| Image Inspection | $2,400/month | $360/month | 85% |
| Invoice Processing | $600/month | $140/month | 77% |
| Total Monthly AI Cost | $4,200 | $680 | 83.8% |
| Annual Savings | - | - | $42,240/year |
| Latency (P95) | 420ms | 180ms | 57% faster |
| Integration Overhead | 5+ API integrations | 1 unified API | 80% less code |
HolySheep Value Proposition:
- ¥1=$1 rate — Save 85%+ versus ¥7.3 standard exchange rates
- Payment flexibility — WeChat Pay and Alipay supported for Chinese market operations
- <50ms infrastructure latency — Direct exchange integrations with Binance, Bybit, OKX, Deribit-style optimized routing
- Free credits on signup — Test the full pipeline before committing
Why Choose HolySheep: Competitive Advantages
- Unified API Architecture: Single
base_url: https://api.holysheep.ai/v1endpoint replaces 5+ fragmented provider integrations, reducing codebase complexity by 40% and eliminating multi-vendor management overhead. - Optimized Model Routing: Automatic task-to-model matching ensures you always use the most cost-effective model for each job. DeepSeek V3.2 ($0.42/MTok) for document OCR, Gemini 2.5 Flash ($2.50/MTok) for image inspection, GPT-4o for voice—each at the right price point.
- Direct Exchange Infrastructure: Leveraging Tardis.dev-style market data relay architecture for <50ms latency, with dedicated capacity for high-throughput property management workloads.
- Regulatory Clarity: ¥1=$1 pricing means transparent, predictable costs without hidden currency conversion fees or跨境 (cross-border) transaction markups.
- Enterprise Procurement Integration: Native support for WeChat Pay and Alipay enterprise accounts streamlines reconciliation for APAC operations.
Common Errors & Fixes
Error 1: Authentication Failed - Invalid API Key
Symptom: {"error": {"message": "Invalid API key provided", "type": "invalid_request_error", "code": "invalid_api_key"}}
Cause: API key not set correctly, expired, or using wrong format.
# WRONG - Hardcoded key in source code
API_KEY = "sk-holysheep-xxxxx" # NEVER do this!
CORRECT - Environment variable or secrets manager
import os
from dotenv import load_dotenv
load_dotenv() # Load .env file
Option 1: Environment variable
client = HolySheepClient(api_key=os.environ.get("HOLYSHEEP_API_KEY"))
Option 2: Secrets manager (AWS Secrets Manager example)
import boto3
client = HolySheepClient(api_key=boto3.client('secretsmanager').get_secret_value('HOLYSHEEP_API_KEY')['SecretString'])
Option 3: Verify key is valid
def verify_holy_sheep_key(api_key: str) -> bool:
"""Test API key validity with a simple request"""
test_client = HolySheepClient(api_key=api_key)
try:
# Make minimal test call
import requests
response = requests.get(
f"{test_client.base_url}/models",
headers=test_client.headers
)
return response.status_code == 200
except:
return FalseError 2: Rate Limit Exceeded - 429 Too Many Requests
Symptom: {"error": {"message": "Rate limit exceeded for model gemini-2.5-flash", "type": "rate_limit_error", "param": null}}
Cause: Concurrent requests exceeding tier limits during batch inspection processing.
# WRONG - Fire-and-forget without rate limiting
async def process_inspections_unsafe(image_urls):
tasks = [client.audit_inspection_images([url]) for url in image_urls]
return await asyncio.gather(*tasks) # Will hit rate limits!
CORRECT - Semaphore-based concurrency limiting
import asyncio
from asyncio import Semaphore
class RateLimitedHolySheepClient:
"""
HolySheep client with built-in rate limiting
Recommended for batch processing inspections
"""
def __init__(self, api_key: str, max_concurrent: int = 10):
self.client = HolySheepClient(api_key=api_key)
self.semaphore = Semaphore(max_concurrent)
self.request_count = 0
self.last_reset = time.time()
self.rate_limit = 100 # requests per minute
async def throttled_audit(self, image_urls: list, inspection_type: str) -> Dict:
"""Rate-limited inspection audit"""
async with self.semaphore:
# Check and reset rate limit counter
current_time = time.time()
if current_time - self.last_reset >= 60:
self.request_count = 0
self.last_reset = current_time
if self.request_count >= self.rate_limit:
wait_time = 60 - (current_time - self.last_reset)
print(f"Rate limit reached. Waiting {wait_time:.1f}s...")
await asyncio.sleep(wait_time)
self.request_count = 0
self.last_reset = time.time()
self.request_count += 1
try:
return await self.client.audit_inspection_images(
image_urls=image_urls,
inspection_type=inspection_type
)
except HolySheepAPIError as e:
if e.status == 429:
# Exponential backoff
await asyncio.sleep(2 ** self.request_count)
return await self.throttled_audit(image_urls, inspection_type)
raise
Usage with rate limiting
async def batch_process_inspections(image_batches: list):
rate_limited_client = RateLimitedHolySheepClient(
api_key=os.getenv("YOUR_HOLYSHEEP_API_KEY"),
max_concurrent=10
)
tasks = [
rate_limited_client.throttled_audit(batch, inspection_type="quarterly_review")
for batch in image_batches
]
results = await asyncio.gather(*tasks, return_exceptions=True)
# Filter out failures
successful = [r for r in results if not isinstance(r, Exception)]
failed = [r for r in results if isinstance(r, Exception)]
print(f"Completed: {len(successful)} successful, {len(failed)} failed")
return successfulError 3: Audio Transcription Timeout on Large Files
Symptom: {"error": {"message": "Request timeout after 30s for audio file > 25MB", "type": "timeout_error"}}
Cause: Audio file exceeds 25MB limit or poor network connectivity.
# WRONG - Direct upload of large audio files
async def transcribe_large_audio(audio_path: str):
# Will timeout for files > 25MB
with open(audio_path, 'rb') as f:
audio_data = f.read()
return await client.transcribe_voice_repair_request(
audio_url=audio_data, # Too large!
property_id="123",
tenant_id="456"
)
CORRECT - Chunked upload with presigned URLs
class ChunkedAudioUploader:
"""
Handles large audio files via chunked upload to HolySheep
Supports files up to 500MB with progress tracking
"""
CHUNK_SIZE = 5 * 1024 * 1024 # 5MB chunks
def __init__(self, api_key: str):
self.api_key = api_key
self.base_url = "https://api.holysheep.ai/v1"
self.headers = {"Authorization": f"Bearer {api_key}"}
async def upload_large_audio(
self,
audio_path: str,
property_id: str,
tenant_id: str,
progress_callback=None
):
"""Upload large audio in chunks, then trigger transcription"""
# Step 1: Initialize multipart upload
async with aiohttp.ClientSession() as session:
init_response = await session.post(
f"{self.base_url}/uploads/initialize",
headers=self.headers,
json={
"filename": os.path.basename(audio_path),
"content_type": "audio/mp4",
"task": "voice_repair_transcription"
}
)
upload_data = await init_response.json()
upload_id = upload_data["upload_id"]
upload_url = upload_data["upload_url"]
# Step 2: Upload chunks
with open(audio_path, 'rb') as f:
chunk_num = 0
while chunk := f.read(self.CHUNK_SIZE):
chunk_response = await session.put(
f"{upload_url}/{chunk_num}",
data=chunk,
headers={"Content-Type": "application/octet-stream"}
)
if chunk_response.status != 200:
raise Exception(f"Chunk {chunk_num} upload failed")
chunk_num += 1
if progress_callback:
progress_callback(chunk_num * self.CHUNK_SIZE)
# Step 3: Complete upload and trigger transcription
complete_response = await session.post(
f"{self.base_url}/uploads/{upload_id}/complete",
headers=self.headers,
json={
"property_id": property_id,
"tenant_id": tenant_id
}
)
return await complete_response.json()
Usage with progress tracking
async def main():
uploader = ChunkedAudioUploader(api_key=os.getenv("YOUR_HOLYSHEEP_API_KEY"))
def progress(loaded):
print(f"Uploaded: {loaded / (100*1024*1024) * 100:.1f}%")
result = await uploader.upload_large_audio(
audio_path="/recordings/weekend_maintenance_call.mp4",
property_id="PROP-001",
tenant_id="TENANT-123",
progress_callback