Engineering AI-Powered, Smart & Scalable Energy Solutions for Sapient Industries
Sapient Industries helps commercial buildings cut energy waste through real-time, device-level insights. As their data and infrastructure needs grew, Intuz stepped in to scale their backend, automate IoT onboarding, and add AI-driven reporting to their cloud platform.
Business & Operation Challenges
Core Challenges Before
the Platform Existed
Problem 1
Slow IoT Device Onboarding
Manual onboarding of IoT devices was slow, inconsistent, and consumed significant engineering time. Every new device required hands-on setup, creating a bottleneck that pulled the team away from higher-priority development work. Device provisioning had become a recurring drain on engineering resources.
problem 2
Rising Cloud Infrastructure Costs
As data volumes increased, cloud storage costs grew without any automated system to manage or clean up redundant data. There were no lifecycle rules in place to control spend. Unchecked infrastructure costs were putting pressure on the business.
Problem 3
Backend Performance Under Pressure
Sapient’s APIs were struggling to handle high volumes of real-time telemetry data from devices across multiple buildings. Bottlenecks were causing delays and affecting the reliability of energy insights delivered to clients. The backend needed to scale without compromising data delivery.
Problem 4
Slow Dashboard Query Performance
MongoDB queries powering Sapient’s dashboards were too slow for a growing, multi-building operation. Users experienced lag when accessing device-level energy data, directly affecting the platform experience. Query performance was holding back the product.
Problem 5
No Automated Reporting for Non-Technical Users
Building managers and non-technical stakeholders had no easy way to understand energy trends without digging into raw data. There was no reporting layer to turn high-frequency telemetry into clear, actionable summaries. The gap between data and decision-making was slowing down operations.
Technical Challenges
Where the Technical Challenges
Were and How We Solved Them
Real-time telemetry at scale, IoT provisioning across hundreds of devices, and AI reporting that non-technical users can actually act on – none of these have off-the-shelf answers.
01
Manual IoT Device Provisioning
Onboarding each IoT device manually took 30 minutes per device and required direct engineering involvement every time – impossible to scale as connected devices grew. Intuz automated the provisioning pipeline using Docker and CI/CD pipelines, bringing device setup time from 30 minutes down to under 3 minutes with secure, reliable connections maintained throughout.
02
High-Volume Telemetry Bottlenecks
Sapient’s existing APIs couldn’t handle the frequency and volume of real-time telemetry data coming in from devices across multiple buildings, causing delays and gaps in energy data. Intuz migrated the heavy-load APIs to FastAPI, enabling parallel data processing and ensuring reliable, uninterrupted delivery of minute-level energy insights.
03
Slow MongoDB Query Performance
MongoDB queries powering Sapient’s dashboards were too slow for a multi-building, high-frequency data environment, causing lag that made real-time monitoring unreliable. Intuz refactored the queries, improved data access patterns, and added optimized indexing – improving dashboard fetch speeds by 3x.
04
High Cloud Storage Costs
Sapient’s AWS S3 storage had accumulated redundant data with no lifecycle rules or tiering strategy, and costs were climbing steadily. Intuz audited the environment, removed redundant data, and introduced automated lifecycle rules with intelligent tiering – delivering a 30% reduction in infrastructure costs.
What the Client Said · Clutch
Their efforts in cloud optimization led to a 30% reduction in our infrastructure costs.
VP of Product
Sapient · Via Clutch Verified Review
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Solution
How We Rebuilt Sapient’s Stack
from the Ground Up
From IoT provisioning to AI-driven reporting, every part of Sapient’s stack needed to be scaled, optimized, or rebuilt – without disrupting live client operations.
GenAI-Driven Reporting Engine
We built an AI engine that automatically reviews high-frequency telemetry data and generates weekly energy and operations reports. Building managers and non-technical teams can now access clear summaries and smart recommendations – turning complex energy data into faster, better decisions.
AWS S3 Cost Optimization
We audited Sapient’s cloud storage, removed redundant data, and introduced automated lifecycle rules with intelligent tiering and better compression. Important energy data stays accessible for dashboards and reports while unnecessary storage costs are eliminated – delivering steady monthly savings.
FastAPI Backend Adoption
We migrated Sapient’s heavy-load APIs to FastAPI, enabling parallel data processing across high volumes of real-time telemetry. The backend now handles frequent, large-scale energy data without bottlenecks, ensuring reliable and uninterrupted delivery of insights to clients.
IoT Device Onboarding Automation
We automated the device provisioning pipeline using Docker and CI/CD pipelines, replacing a slow and inconsistent manual process. Device setup time dropped from 30 minutes to under 3 minutes, with secure and reliable connections maintained for every device onboarded.
Frontend Visualization Enhancements
We redesigned Sapient’s React frontend to simplify the interface, improve charts, and organize information more clearly. Optimized rendering now gives users near-instant updates, making complex energy data easy to understand and act on quickly.
Real-Time Data Pipeline Fix
We replaced Sapient’s old telemetry pipeline with a real-time, resilient system built to handle minute-level data without interruption. Advanced caching and error handling keep live device data accurate, giving clients reliable energy insights for better facility management.
MongoDB Query Optimization
We refactored Sapient’s MongoDB queries, improved data access patterns, and added optimized indexing across the database. Dashboard fetch speeds improved by 3x, enabling real-time updates across large multi-building telemetry sets without lag.
What the Client Reported
They’ve contributed to front-end and back-end development, IoT systems, big data pipelines, and device firmware, while also helping us reduce cloud infrastructure costs and explore AI-driven solutions.
VP of Product
Sapient · Via Clutch Verified Review
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Tools & technologies
Tools & Technologies We Used
Sapient’s stack required a precise mix of modern frameworks, cloud infrastructure, and AI tooling to handle real-time energy data at scale.
What Our Client Had to Say
They have been an exceptional partner across multiple facets of our technology stack. They’ve contributed to front-end and back-end development, IoT systems, big data pipelines, and device firmware, while also helping us reduce cloud infrastructure costs and explore AI-driven solutions. In addition to their technical capabilities, they’ve been highly flexible, proactive in managing deliverables, and excellent at converting high-level business goals into practical technical implementations. Their collaborative approach, reliability, and ability to scale with our needs have made them an invaluable extension of our team.”
VP of Product
IOT PLATFORM Company, USA · CLUTCH VERIFIED REVIEW
