Automating Quality Control at Scale

We built Exacto, an enterprise IoT edge intelligence platform that replaced manual quality sampling with 100% automated visual inspection.

Client

Exacto

Industry

Manufacturing

Solution

Enterprise IoT edge intelligence platform

Overview

Development of an Enterprise IoT Edge Intelligence Platform for AI-Powered Visual Inspection

We built Exacto from the ground up, an enterprise IoT edge intelligence platform that replaces manual quality sampling with 100% automated visual inspection on live production lines. The platform connects edge devices, IP cameras, and PLCs to a centralized dashboard where ML models are deployed, inspections run in real time, and every result is tracked and audited. Outcomes: device commissioning reduced from multi-day scripting to a single guided session, quality reporting shifted from weekly manual summaries to live dashboards, and compliance audit preparation cut dramatically with a built-in audit trail and on-demand PDF export.

Project at a Glance

The Challenge

Manufacturing environments offered no single standard to build against.

The Solution

We delivered a full-stack IoT intelligence platform built around nine integrated modules.

Services

Technologies

Apache Solr, OPC UA, Modbus, AWS S3, On-premise NAS

Challenge

The Challenging Part

Manufacturing environments offered no single standard to build against. Exacto had to unify heterogeneous hardware, different camera brands, GigE/USB/TCP-IP protocols, image formats, and PLC standards (OPC UA, Modbus), into one structured onboarding flow without losing device-level precision. Beyond hardware, inspection results needed to reach the dashboard the instant they occurred on the production line, requiring a dual-path architecture combining real-time polling and background cron-based sync.

Heterogeneous edge hardware with no unified protocol or image standard across camera brands and PLCs Real-time inspection results required without cloud latency, ML inference running on-device, syncing centrally Millions of inspection records (cycle counters, batch IDs, model outputs, timestamps, images) outpacing relational query performance ML model lifecycle management, deploying the right model to the right device for the right part number without disrupting production Compliance requirements demanding complete, exportable audit trails for every user action across the platform Flexible data storage, some clients require AWS S3, others mandate fully on-premise NAS for data sovereignty

Inside the Product

A closer look

Solution

How the Solution Worked

We delivered a full-stack IoT intelligence platform built around nine integrated modules.

Device onboarding module | Guides operators through a 7-step wizard, from bare hardware registration to a fully operational ML inspection station. Real-time production monitoring | Streams pass/fail results and live FPY calculations per camera every four seconds. Centralized ML model library | Manages version tracking, file uploads, and device-level deployment mapping. Inspection results and reporting | Generates PDF exports ready for compliance reviews, while the built-in audit log captures every create, update, delete, and override action.

Results

Real Impact for the Business

100% | inspection coverage across all production cycles 1M+ | inspection records in seconds Up to 6 cameras | per device managed through one unified form Device commissioning reduced | from multi-day manual scripting to a single guided session ML deployment errors eliminated | centrally version-tracked and audited Quality reporting shifted to live dashboards | real-time FPY data filterable by shift, part number, and model

Client Testimonial