Custom AI and ML Model Development
Off-the-shelf AI models solve generic problems. We build custom AI and ML models trained on your data to solve your specific business problems.
Trusted by Enterprises in 30+ Countries
OUR SERVICES
Custom AI and ML Model Services We Deliver
Custom Model Development
We design and train custom AI and ML models on your data to deliver the accuracy your use case requires.
Model
Fine-Tuning
We fine-tune pre-trained models on your data so outputs are accurate and aligned to your context.
ML Pipeline Development
We build end-to-end ML pipelines covering data ingestion, training, evaluation, and deployment in one automated workflow.
MLOps and Model Management
We implement MLOps infrastructure to track performance, trigger retraining, and keep models reliable after deployment.
OUR SOLUTIONS
Our Custom AI and ML Model Solutions
Predictive Analytics Models
Natural Language Processing
Computer Vision Models
Visual data from cameras and scans often goes unanalysed. We build computer vision models for defect detection, object recognition, and quality inspection.
Recommendation Systems
Generic recommendations leave revenue uncaptured. We build recommendation models trained on your user and product data to surface what each customer actually wants.
Anomaly Detection Models
Time Series Forecasting
OUR PROCESS
How We Implement Custom AI and ML Models
Our Approach
We start by defining the business problem the model needs to solve and assessing whether the available data can support it. Most ML projects that fail do so because the problem was too loosely defined or the data quality was never properly evaluated before the build started.
Key Activities:
- Business problem scoping and success criteria
- Data availability and quality assessment
- Feature identification and data gap analysis
- Model approach selection and feasibility review
Our Approach
Model performance is determined more by data quality and feature design than by algorithm choice. We invest heavily in preparing clean, well-structured training data and engineering the features that give the model the best possible signal to learn from.
Key Activities:
- Data cleaning and normalisation
- Feature engineering and selection
- Training, validation, and test set construction
- Data augmentation where required
Our Approach
We develop and evaluate models iteratively — training candidates, testing against real business scenarios, and refining until performance meets the defined success criteria. Evaluation is done against metrics that reflect actual business value, not just technical benchmarks.
Key Activities:
- Model training and hyperparameter tuning
- Cross-validation and performance benchmarking
- Bias and fairness testing
- Business scenario and edge case evaluation
Our Approach
We deploy models into production environments with the inference infrastructure, API layers, and integrations needed to make them accessible to the systems and teams that depend on them. Deployment is treated with the same rigour as the model build itself.
Key Activities:
- Model serving and inference infrastructure
- API development and system integration
- Performance and latency optimisation
- Staged rollout and go-live support
Our Approach
Models degrade as data distributions shift. We set up monitoring to track prediction quality, data drift, and model performance over time — and retraining pipelines that keep models accurate as the business and its data evolve.
Key Activities:
- Model performance and drift monitoring
- Automated alerting on degradation
- Scheduled and triggered retraining pipelines
- Model versioning and rollback capability
Our Approach
We start by defining the business problem the model needs to solve and assessing whether the available data can support it. Most ML projects that fail do so because the problem was too loosely defined or the data quality was never properly evaluated before the build started.
Key Activities:
- Business problem scoping and success criteria
- Data availability and quality assessment
- Feature identification and data gap analysis
- Model approach selection and feasibility review
Our Approach
Model performance is determined more by data quality and feature design than by algorithm choice. We invest heavily in preparing clean, well-structured training data and engineering the features that give the model the best possible signal to learn from.
Key Activities:
- Data cleaning and normalisation
- Feature engineering and selection
- Training, validation, and test set construction
- Data augmentation where required
Our Approach
We develop and evaluate models iteratively — training candidates, testing against real business scenarios, and refining until performance meets the defined success criteria. Evaluation is done against metrics that reflect actual business value, not just technical benchmarks.
Key Activities:
- Model training and hyperparameter tuning
- Cross-validation and performance benchmarking
- Bias and fairness testing
- Business scenario and edge case evaluation
Our Approach
We deploy models into production environments with the inference infrastructure, API layers, and integrations needed to make them accessible to the systems and teams that depend on them. Deployment is treated with the same rigour as the model build itself.
Key Activities:
- Model serving and inference infrastructure
- API development and system integration
- Performance and latency optimisation
- Staged rollout and go-live support
Our Approach
Models degrade as data distributions shift. We set up monitoring to track prediction quality, data drift, and model performance over time — and retraining pipelines that keep models accurate as the business and its data evolve.
Key Activities:
- Model performance and drift monitoring
- Automated alerting on degradation
- Scheduled and triggered retraining pipelines
- Model versioning and rollback capability
WHY IT MATTERS
Benefits of Custom AI and ML Model Development
Models That Fit
Proprietary Data
Accuracy Where It Matters
Generic models optimise for average performance across many use cases. Custom models optimise for your specific task and the accuracy your decisions require.
Production Reliability
Models built with proper pipelines, monitoring, and retraining infrastructure perform reliably over time rather than degrading quietly after deployment.
Seamless Integration
Ongoing Improvement
Custom models improve as more data becomes available. With the right retraining pipelines, model performance compounds with every new data point collected.
Latest Case Studies
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Rebuilt and unified parcel operations for an Australian logistics network across 1,700+ locations
Rebuilt and unified a parcel management platform for an Australian logistics network across 1,700+ locations.
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Designed and built every internal clinical system for Australia's leading cancer centre
Built clinical systems, MDT workflows, and an analytics platform for Australia's leading cancer centre.
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Engineered a joint replacement care platform connecting surgeons, engineers, and patients across 17,350 cases
Built a joint replacement care platform connecting surgeons, engineers, radiologists, and patients across 17,350 cases.
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Delivered a multi-phase order lifecycle platform for a global industrial manufacturer
Built a multi-phase order lifecycle automation platform for a global industrial manufacturer across eight years.
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Eliminated IT dependency for Kaizen teams across 30+ manufacturing sites
Built a self-service workflow builder enabling Kaizen teams across 30+ sites to work without IT.
Read Full Case Study →
TESTIMONIALS
Businesses Worldwide Trust Classic Informatics
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PARTNER WITH US
Why Classic Informatics?
Value Beyond Code
Real-time data and modern systems give your teams the visibility to act.
Deep Tech Expertise
20+ years across legacy, data, and AI — the hard problems aren't new to us.
Built for Growth
We align every solution to your business goals, not just your tech stack.
Reliable Delivery
Expert teams who move fast, communicate clearly, and deliver on time.
AI-First Approach
Every solution we build is designed with AI in mind, from architecture to delivery.
Complete Transparency
You know what we're building, why, and what it costs — at every stage.
FAQS
Frequently Asked Questions
Build AI Models Trained on Your Data, for Your Business
From problem scoping to production deployment and beyond.
