Custom ML Models
That See What Humans Miss
Your data contains patterns worth millions. Patterns your spreadsheets will never reveal. Our PhD-level ML engineers build production-grade models that predict customer churn before it happens, detect fraud in milliseconds, and surface opportunities buried in petabytes of noise.
Your Data Is a Goldmine You Cannot Access
These are the symptoms of an organization drowning in data but starving for intelligence.
Decisions Based on Gut Feel
Your executives are making million-dollar decisions based on intuition and last quarter's reports. By the time you see the trend, your competitors already acted on it weeks ago.
BI Dashboards That Show the Past
Beautiful charts telling you what already happened. You need models that predict what will happen next -- demand spikes, customer churn, equipment failures, fraud attempts.
Failed ML Projects Burning Budget
87% of ML projects never reach production. Your internal team built a proof-of-concept that worked in Jupyter notebooks but crumbled under real-world data. Sound familiar?
Months-Long Model Development
Your data science team is 6 months into a project with no production deployment in sight. Feature engineering is a nightmare. Model drift is unmanaged. There is no MLOps pipeline.
Cannot Find ML Talent
Hiring a senior ML engineer costs $180K-$300K/year. Finding one who also understands your industry domain, cloud infrastructure, and MLOps? Nearly impossible in this market.
Competitors Already Using ML
While you debate whether to invest in ML, your competitors are already using it for dynamic pricing, personalized recommendations, predictive maintenance, and fraud detection.
Free ML Feasibility Assessment ($5,000 Value)
We will analyze your data landscape, identify the 3 highest-ROI ML use cases, and deliver a technical feasibility report -- completely free. Limited to 5 assessments per month.
2 of 5 February slots already claimed. No commitment required.
ML Models We Engineer
From classical ML to cutting-edge deep learning, deployed on production-grade MLOps infrastructure.
Predictive Analytics
Demand forecasting, customer lifetime value prediction, churn prediction, price optimization, and risk scoring. Models that turn your historical data into future revenue.
Natural Language Processing
Sentiment analysis, document classification, entity extraction, summarization, and semantic search. Multilingual models trained on APAC language patterns.
Recommendation Engines
Collaborative filtering, content-based, and hybrid recommendation systems. Increase average order value by 25-40% and engagement by 60% with personalized suggestions.
Anomaly Detection
Fraud detection, cybersecurity threat identification, equipment failure prediction, and quality control. Catch the 0.01% of events that cost you millions.
Time-Series Forecasting
Revenue forecasting, inventory optimization, resource planning, and market prediction using LSTM, Prophet, and Transformer-based architectures.
MLOps & Model Management
End-to-end MLOps on AWS SageMaker, GCP Vertex AI, or Azure ML. Automated retraining pipelines, drift detection, A/B testing, and model versioning in production.
From Raw Data to Production Model
Our proven 5-phase methodology has delivered 83 production ML models with zero failures in production.
Data Discovery
Audit your data estate, assess quality, identify gaps, and map the highest-ROI ML opportunities. Feasibility report with accuracy estimates delivered.
WEEK 1-3
Feature Engineering
Transform raw data into predictive features. Data cleaning, normalization, synthetic data generation, and pipeline architecture design.
WEEK 3-6
Model Development
Experiment with multiple architectures, hyperparameter tuning, cross-validation, and ensemble methods. We test 50+ model configurations per project.
WEEK 6-12
Production Deploy
MLOps pipeline setup, containerized deployment, real-time inference APIs, monitoring dashboards, and automated fallback mechanisms.
WEEK 12-16
Continuous Learning
Automated retraining on new data, drift detection, performance monitoring, model versioning, and quarterly optimization reviews.
ONGOING
ML Project Investment Tiers
Transparent pricing with performance guarantees. Every engagement starts with a free feasibility assessment.
ML Pilot
- Single ML model (1 use case)
- Data assessment & preparation
- Feature engineering pipeline
- Model training & validation
- Basic API deployment
- Performance monitoring dashboard
- 90-day model support
- 92% accuracy guarantee
ML Growth
- Up to 3 ML models
- Full data engineering pipeline
- Advanced feature engineering
- Ensemble & deep learning models
- Full MLOps pipeline (CI/CD for ML)
- Automated retraining & drift detection
- A/B testing framework
- 6-month dedicated support
- Priority 4h response time
- Quarterly optimization reviews
ML Transformation
- Unlimited ML models
- Custom data lake architecture
- Real-time streaming ML pipelines
- Custom LLM fine-tuning
- On-premise / private cloud deployment
- Dedicated ML engineering team
- 24/7 SLA-backed support
- 12-month continuous optimization
- Executive ML strategy sessions
- Team training & knowledge transfer
All prices in USD. Q1 2026 early-bird rates end February 28. Milestone-based billing available.
ML Models by Industry
Domain expertise is what separates a model that works in a lab from one that works in production.
Financial Services
Credit scoring, fraud detection, algorithmic trading signals, anti-money laundering, loan default prediction, and customer lifetime value models. Regulatory-compliant with full model explainability for auditors.
CASE: 99.2% fraud detection, 0.3% false positive rate
E-Commerce & Retail
Demand forecasting, dynamic pricing, product recommendations, customer segmentation, churn prediction, and inventory optimization. Models that directly impact your revenue line.
CASE: 38% AOV increase via recommendation engine
Healthcare & Life Sciences
Clinical outcome prediction, drug interaction detection, patient risk stratification, readmission prediction, and medical NLP for clinical notes. HIPAA-compliant, FDA-aware model development.
CASE: 94% readmission prediction accuracy
Manufacturing & Supply Chain
Predictive maintenance, yield optimization, demand planning, quality prediction, and supplier risk assessment. Models that prevent downtime and optimize throughput across your operations.
CASE: 67% reduction in unplanned downtime
Telecom & Media
Network anomaly detection, subscriber churn prediction, content recommendation, bandwidth optimization, and customer experience scoring. Models tuned for APAC telecom market dynamics.
CASE: $2.3M ARR saved via churn prediction
Energy & Utilities
Load forecasting, grid optimization, renewable energy prediction, equipment failure detection, and consumption pattern analysis. Models that reduce waste and optimize energy distribution.
CASE: 23% improvement in load forecasting accuracy
// IN PLAIN LANGUAGE
Imagine having a team of analysts who have read every transaction, every customer interaction, and every data point your company has ever generated -- and can predict what happens next.
That is what a custom ML model does. It finds the patterns buried in millions of data points that no human team could ever process, and turns those patterns into predictions you can act on today.
92% Accuracy Guarantee or We Work Free
If your custom ML model does not achieve the agreed accuracy threshold within the validation period, we continue optimizing at zero additional cost until it does. We have never failed to meet this guarantee.
Results From Our ML Models
"Seraphim's churn prediction model identified at-risk customers 45 days before cancellation. We saved $2.3M in annual recurring revenue in the first 6 months alone. The model accuracy exceeded 97%. Worth every dollar of the investment."
Lisa Wong
VP Data Science, TelcoMax Singapore
"Their recommendation engine increased our average order value by 38% and repeat purchases by 52%. The team delivered a production-ready model in 14 weeks, complete with A/B testing infrastructure. This is what ML done right looks like."
Hiroshi Nakamura
CTO, FashionForward Tokyo
"We had tried building fraud detection internally for 8 months and failed. Seraphim's team built a model that catches 99.2% of fraudulent transactions with a 0.3% false positive rate. The system processes 50K transactions per minute in real time."
Rajesh Kumar
Head of Risk, PaySecure Malaysia
Every Day Without ML, Your Data Depreciates
The patterns in your data are time-sensitive. Customer behavior shifts. Market dynamics change. The longer you wait to deploy ML models, the less valuable your historical data becomes. Start now and compound your advantage.
Machine Learning FAQs
What types of ML models do you build?
+We build the full spectrum: predictive analytics (regression, classification), NLP/NLU models (sentiment, entity extraction, summarization), recommendation engines (collaborative, content-based, hybrid), anomaly detection (fraud, cybersecurity, quality control), time-series forecasting (LSTM, Prophet, Temporal Fusion Transformers), and custom deep learning architectures. Every model is tailored to your specific business domain and data characteristics.
How much data do we need for a custom ML model?
+It depends on the use case. Simple classification tasks might need 5,000-10,000 labeled examples. Complex deep learning models can require 100,000+. However, we specialize in techniques for data-scarce environments: transfer learning from pre-trained models, synthetic data generation, data augmentation, and few-shot learning. During our free assessment, we evaluate your data volume and recommend the optimal approach.
What accuracy levels can we expect?
+We guarantee a minimum of 92% accuracy on agreed-upon metrics (precision, recall, F1, AUC-ROC, etc.) or we continue optimizing at no additional cost. In practice, most of our production models achieve 94-98% accuracy. During the feasibility assessment, we provide accuracy estimates based on your data characteristics before you commit.
How do you handle model deployment and MLOps?
+We implement full MLOps pipelines on your preferred cloud (AWS SageMaker, Azure ML, GCP Vertex AI). This includes CI/CD for ML models, automated data validation, scheduled and triggered retraining, drift detection and alerting, A/B testing for model versions, real-time inference APIs with auto-scaling, and comprehensive monitoring dashboards. Every model is containerized and version-controlled.
Can you work with our existing data infrastructure?
+Absolutely. We integrate with any data stack: Snowflake, Databricks, BigQuery, Redshift, PostgreSQL, MongoDB, Elasticsearch, Apache Kafka, custom data lakes, and legacy systems. If your data engineering needs improvement, we can also build or optimize your data pipelines as part of the engagement.
What is the typical timeline for a custom ML project?
+A standard ML project takes 10-20 weeks from discovery to production deployment. Pilot projects (single model) can be as fast as 10 weeks. Enterprise transformations with multiple models and custom infrastructure typically take 16-24 weeks. We use agile sprints with bi-weekly demos so you see tangible progress from week one.
Do you provide knowledge transfer to our internal team?
+Yes. Our Enterprise tier includes full knowledge transfer: documentation, architecture walkthroughs, hands-on training sessions for your data science team, and ongoing mentoring. We want you to be self-sufficient in maintaining and extending your ML systems. We also offer standalone ML training workshops.
Why Companies Choose Our ML Team
PhD-Level Engineering
Our ML team includes 12 PhD-level engineers from institutions like NUS, KAIST, Tokyo Institute of Technology, and Tsinghua. Deep theoretical knowledge combined with production engineering experience.
87% of ML Projects Fail. Ours Do Not.
The industry-wide ML project failure rate is staggering. Our success rate is 100% in reaching production deployment. The difference? Rigorous feasibility assessment, proper data engineering, and battle-tested MLOps from day one.
Production-First Mindset
We do not build models that work in Jupyter notebooks and fail in production. Every model is designed for real-world data distribution, edge cases, scale, and drift from the very first experiment.
Full MLOps Included
Model deployment is not the end -- it is the beginning. We include complete MLOps: CI/CD pipelines, automated retraining, drift detection, A/B testing, and monitoring. Your models improve continuously.
Domain Expertise Across APAC
83 models in production across finance, retail, healthcare, manufacturing, and telecom in 10 APAC markets. We understand the data patterns and regulatory requirements unique to each sector and geography.
Accuracy Guaranteed by Contract
92% minimum accuracy guaranteed. We have never failed to meet this threshold. If we do, we continue optimizing at zero cost. Most clients see 94-98% accuracy in production.
Ready to Unlock Your Data's Potential?
Free ML feasibility assessment valued at $5,000. We analyze your data landscape, identify the highest-ROI ML use cases, and deliver a technical roadmap -- whether you work with us or not.