AI/ML Development Services
We design, build, and deploy machine learning systems that move beyond prototypes — into production environments where they generate real business value.
Classic Informatics helped us speed up development, enhance our product, and expand partnerships with global industry leaders.
David McLean, CEO, Hubbed,
Rated 4.9/5 Stars on Clutch
Rated 4.9/5 on Goodfirms
Trusted by Enterprises in 30+ Countries
OUR SERVICES
AI/ML Development Services We Deliver
ML Development & MLOps
Custom models built for your data, deployed to production, and kept accurate automatically.
Computer
Vision
Visual intelligence systems for defect detection, object recognition, and real-time image analysis.
Predictive Analytics
Forecasting models that turn historical data into forward-looking decisions across ops, finance, and supply chain.
NLP
Engineering
Text and language models for document processing, sentiment analysis, and intelligent search at scale.
OUR SOLUTIONS
Our AI/ML Development Solutions
Predictive Maintenance Models
Underwriting Risk Scoring Models
Clinical Outcome Prediction Models
Demand & Supply Forecasting Models
Fraud & Anomaly Detection Systems
Document Intelligence & NLP Pipelines
They extract structured data — names, dates, amounts, diagnoses, clauses — from unstructured documents at the scale your operations generate every day.
HOW WE WORK
How We Implement AI/ML Solutions
Our Approach
Before writing a single line of model code, we work with your team to define the exact business problem, identify the right data sources, and validate that machine learning is the right tool for the job.
Key Activities:
- Map business goal to ML problem type
- Audit available data for quality and coverage
- Define success metrics and acceptance criteria
- Confirm feasibility with a scoped proof of concept
Our Approach
Clean, well-structured data is what separates models that work from models that don't. We build the preprocessing pipelines that get your data into a reliable, training-ready state before any modelling begins.
Key Activities:
- Extract and consolidate data from source systems
- Clean, normalize, and handle missing values
- Engineer features relevant to the target outcome
- Version datasets for reproducible training runs
Our Approach
We select and train algorithms matched to your problem — whether that's classification, regression, time-series forecasting, computer vision, or NLP. Every model is evaluated against your defined metrics, not generic benchmarks.
Key Activities:
- Select and baseline candidate algorithms
- Train, tune, and validate models iteratively
- Run explainability checks on model decisions
- Document model performance for stakeholder review
Our Approach
A model only creates value when it's running inside your real systems. We handle deployment to cloud or on-premise environments and integrate model outputs into the workflows and applications your teams already use.
Key Activities:
- Package and containerize models for production
- Build REST APIs or batch inference pipelines
- Integrate predictions into existing business systems
- Validate end-to-end performance in staging environment
Our Approach
Models degrade as real-world data changes. We build the monitoring infrastructure that tracks prediction accuracy, detects data drift, and triggers retraining workflows automatically — so your models stay accurate without manual oversight.
Key Activities:
- Set up model performance and drift monitoring
- Automate retraining and validation pipelines
- Configure alerting for accuracy degradation
- Maintain model versioning and rollback capability
Our Approach
Before writing a single line of model code, we work with your team to define the exact business problem, identify the right data sources, and validate that machine learning is the right tool for the job.
Key Activities:
- Map business goal to ML problem type
- Audit available data for quality and coverage
- Define success metrics and acceptance criteria
- Confirm feasibility with a scoped proof of concept
Our Approach
Clean, well-structured data is what separates models that work from models that don't. We build the preprocessing pipelines that get your data into a reliable, training-ready state before any modelling begins.
Key Activities:
- Extract and consolidate data from source systems
- Clean, normalize, and handle missing values
- Engineer features relevant to the target outcome
- Version datasets for reproducible training runs
Our Approach
We select and train algorithms matched to your problem — whether that's classification, regression, time-series forecasting, computer vision, or NLP. Every model is evaluated against your defined metrics, not generic benchmarks.
Key Activities:
- Select and baseline candidate algorithms
- Train, tune, and validate models iteratively
- Run explainability checks on model decisions
- Document model performance for stakeholder review
Our Approach
A model only creates value when it's running inside your real systems. We handle deployment to cloud or on-premise environments and integrate model outputs into the workflows and applications your teams already use.
Key Activities:
- Package and containerize models for production
- Build REST APIs or batch inference pipelines
- Integrate predictions into existing business systems
- Validate end-to-end performance in staging environment
Our Approach
Models degrade as real-world data changes. We build the monitoring infrastructure that tracks prediction accuracy, detects data drift, and triggers retraining workflows automatically — so your models stay accurate without manual oversight.
Key Activities:
- Set up model performance and drift monitoring
- Automate retraining and validation pipelines
- Configure alerting for accuracy degradation
- Maintain model versioning and rollback capability
WHY IT MATTERS
Benefits of AI/ML Engineering
Decisions Grounded in Data
Models That Stay Accurate
Faster Time to Insight
AI Built for Your Data
Less Manual Work
Full Model Visibility
Latest Case Studies
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Rebuilt and unified parcel operations for an Australian logistics network across 1,700+ locations
Unified 1,700+ partner locations and five carrier integrations into a single parcel management platform, processing 10,000+ daily transactions.
Read Full Case Study → -
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Replaced manual loan workflows with a custom platform for a Danish lender
Automated loan origination, credit checks, and document generation for a Danish property credit platform.
Read Full Case Study → -
Delivered a wear analysis and lifecycle management platform for a global mining manufacturer
A third-party tool replaced with a purpose-built inspection and wear platform tracking 900+ mining assets at global mine sites.
Read Full Case Study → -
Delivered three generations of analytics infrastructure for a 250-clinic dental group
Three generations of analytics infrastructure built over 20 years, delivering daily operational dashboards across 250 dental clinics and 1,000+ providers.
Read Full Case Study → -
Designed and built every internal clinical system for Australia's leading cancer centre
Clinical systems, MDT workflows, staff compliance, and an Azure analytics platform built from scratch for a specialist cancer centre across 9 departments.
Read Full Case Study →
TESTIMONIALS
Businesses Worldwide Trust Classic Informatics
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
Is Your ML System Actually Delivering Value in Production?
We find what's broken and get it live.
