AI/ML production expertise
Production AI systems
Monthly predictions served
Industries automated
Partner with Ciel Technology for end-to-end AI—from data pipeline to production inference—without the 87% failure rate of typical ML projects.
Senior ML engineers with production systems at petabyte scale and billion-user products.
Models built for production from day one with automated retraining, A/B testing, and monitoring.
Every model optimized for your specific success metrics, not academic benchmarks.
Ciel Technology delivers complete AI solutions from data ingestion to real-time inference at scale.
Bespoke ML models trained on your proprietary data for specific business outcomes.
Capabilities include:
Advanced NLP systems for text understanding, sentiment analysis, and conversational AI.
What we offer:
Production computer vision solving real business problems from defect detection to facial recognition.
Our services:
Complete ML infrastructure for continuous model training, validation, and deployment.
Expertise includes:
Production-proven tools for enterprise ML at scale.
PyTorch 2.1
TensorFlow 2.15
JAX
Hugging Face Transformers
MLflow
Kubeflow
ClearML
AWS SageMaker
GCP Vertex AI
Azure ML
Apache Spark
Dask
Ray
Triton Inference Server
KServe
TensorFlow Serving
Ciel AI powers intelligent automation for fintech, healthcare, education, and e-commerce globally.
Data audit, labeling strategy, pipeline architecture, success metric definition.
Baseline → SOTA → custom fine-tuning → business validation.
Automated retraining, A/B testing framework, monitoring dashboards.
Low-latency inference, cost optimization, compliance validation.
Stakeholder dashboards, decision automation, continuous improvement.
Schedule an AI strategy session with our Head of Machine Learning and CTO.
Industry: Social/Messaging
Platforms: iOS, Android
Built contextual video recommendation engine classifying social media content in real-time using multimodal NLP+CV. Parses messages like “raining in London” → location: London, category: weather. 85% classification accuracy across 10M+ videos.
🔗 App Store
Industry: Education/Research
Amazon Mechanical Turk survey classification using Python NLTK and custom transformers. Automated tagging of 50K+ car survey responses into behavioral segments with 92% F1 score. Enabled breakthrough mobility research insights.
🔗 Erasmus University
Industry: FinTech/Banking
Real-time transaction fraud detection using XGBoost + LSTM hybrid model with 0.3% false positive rate. Analyzes 10K+ features including device fingerprinting, behavioral biometrics, and blockchain patterns. Saved $2.3M in first year.
🔗 Banking Platform
Industry: Web3 Gaming
Computer vision model predicting NFT rarity scores from pixel art analysis + marketplace sentiment analysis. Enabled 47% better investment decisions for collectors. Deployed on Solana with sub-second inference.
🔗 Game Platform
Industry: Gig Economy
Recommendation system matching customers with providers using collaborative filtering + geolocation + NLP job description parsing. 3.8x match acceptance rate improvement over baseline heuristics.
🔗 Live Platform
95%+ model performance maintained 12+ months post-deployment.
Inference optimization reducing cloud GPU costs 90% without accuracy loss.
80%+ manual processes replaced with AI-driven automation.
Clear KPI improvement roadmap with 3/6/12 month business impact projections.
Custom 4x better accuracy on proprietary data, 87% of generic models fail in production.
Data-ready: 8 weeks. Data pipeline needed: 16 weeks. Greenfield: 6 months. MVP guarantee in 90 days.
92-97% production accuracy across classification, detection, forecasting tasks with proper data.
Complete MLOps: automated drift detection, retraining pipelines, champion/challenger validation.
TensorFlow Lite, CoreML, ONNX Runtime with 4ms inference on iPhone 12, quantized INT8 models.
Federated learning, differential privacy, on-premise deployment, SOC2 Type II compliance standard.
Business KPIs only: revenue lift, cost savings, process efficiency—not academic benchmarks.
Hybrid: development in cloud, inference where data sovereignty/compliance requires it.
Seamless integration with Tableau, PowerBI, Looker via REST APIs and scheduled model updates.
24/7 monitoring, 99.9% inference uptime, weekly accuracy reports, quarterly retraining cycles.
