Case Studies

Real systems. Real results.

We don't show concept demos. These are production systems running live, processing real data, and delivering measurable value.

NLP · Document AIProduction

AI Resume Screener & Parser

An intelligent document pipeline that extracts structured data from resumes, scores candidates against job descriptions, and integrates with existing ATS workflows. Built for TalentScale Systems, processing 50K+ documents monthly.

System Architecture: NLP · Document AIProduction Deployed

94.2%

Extraction Accuracy

85%

Time Saved

50K+

Monthly Documents

6 wk

Time to ROI

Challenge

The client's existing keyword-matching system was rejecting qualified candidates and passing unqualified ones. Manual review was taking 40+ hours per week.

Approach

We designed a multi-stage pipeline: OCR for non-digital documents, a custom NER model for entity extraction, and a BERT-based scoring model trained on TalentScale's historical hiring data.

Result

94.2% extraction accuracy across 23 field types. Reduced manual review time by 85%. ROI positive within 6 weeks of deployment.

“Aether reduced our manual screening bottleneck by 85% within 6 weeks of live deployment. Their custom NER and scoring models outclassed every commercial API we benchmarked.”

Marcus Vance·VP of Engineering & Platform·TalentScale Systems

LLMNLPBERTNERFastAPIPostgreSQL

Computer Vision · OCRProduction

DOX — Smart Document Classifier

Classifies and routes incoming documents by type — invoices, contracts, forms, receipts — using a multi-modal CNN architecture with OCR augmentation for Apex Financial Logistics.

System Architecture: Computer Vision · OCRProduction Deployed

97.1%

Classification Rate

Document Types

1.1s

Avg. Latency

0.3%

Error Rate

Challenge

200+ staff hours per month spent manually sorting and routing incoming documents across 12 departments. Error rate of 8%+ on manual routing.

Approach

We built a dual-path classifier: a ResNet backbone for visual features combined with an OCR+BERT pathway for text features. The two streams are fused at inference for robust classification even with poor scan quality.

Result

97.1% classification rate across 12 document types. Processing time under 1.1 seconds. Deployed as a containerised microservice with auto-scaling.

“Deploying DOX dropped our routing error rate from 8.4% to under 0.3%. The multimodal vision+OCR architecture processes hundreds of thousands of multi-page invoices with zero human intervention.”

Elena Rostova·Chief Technology Officer·Apex Financial Logistics

OCRCNNResNetBERTDockerKubernetes

Edge AI · ManufacturingProduction

Visual Quality Control System

Real-time defect detection for an automated manufacturing line — runs on edge devices, flags anomalies in under 200ms, and feeds data into a central analytics dashboard for Aerotech Precision Manufacturing.

System Architecture: Edge AI · ManufacturingProduction Deployed

99.3%

Detection Rate

<200ms

Edge Inference

24/7

Uptime

73%

Returns Reduced

Challenge

Manual quality inspection was catching only 82% of defects, with false positive rates high enough to slow production. The client needed sub-200ms inference on low-power edge hardware.

Approach

Custom YOLOv8 model trained on 15K annotated defect images. Model quantised to INT8 for NVIDIA Jetson deployment. Inference pipeline includes frame buffering, confidence thresholding, and MQTT-based alerting.

Result

99.3% defect detection rate with <0.5% false positives. Running 24/7 on 6 production lines. Reduced quality-related returns by 73%.

“The sub-20ms inference loop running locally on our Jetson edge devices caught defects our manual QA missed for years. Aether delivered a real industrial computer vision system that runs 24/7 without a single failure.”

Dr. Dieter Krause·Head of Industrial Automation·Aerotech Precision Manufacturing

YOLONVIDIA JetsonMQTTEdge AIINT8 Quantisation

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