Gen AI, LLM & Agentic AI Development Services
Most AI experiments never reach production. We build generative AI, LLM, and agentic systems that work reliably in real business environments.
Trusted by Enterprises in 30+ Countries
OUR SERVICES
Generative AI Development Services We Deliver
Generative AI Consulting
We assess your use cases and readiness to define a practical build roadmap.
LLM Integration & Development
We build the integration layer that connects language models to your data, workflows, and systems.
Agentic AI Development
We build AI agents with the right tool access, memory, and guardrails.
RAG System Development
We build retrieval-augmented generation systems grounded in your organisation's specific knowledge and context.
OUR SOLUTIONS
Our Gen AI and Agentic AI Solutions
AI Copilots and
Assistants
Automated Document Processing
Multi-Agent
Orchestration
Complex workflows exceed what single agents can handle. We design multi-agent architectures that coordinate tasks reliably across systems and data sources.
LLM-Powered Search and Discovery
Keyword search returns results. LLM-powered search understands intent. We build intelligent search that surfaces the most relevant information from your organisation's data.
AI Workflow
Automation
Gen AI for Customer Experiences
OUR PROCESS
How We Implement Generative AI and Agentic AI Systems
Our Approach
We start by identifying which use cases are genuinely suited to generative AI or agentic approaches, and whether the data and infrastructure required to support them are in place. Most failed AI projects start with the wrong use case or the wrong assumptions about what the data can support.
Key Activities:
- Use case prioritisation and value mapping
- Data availability and quality assessment
- Model approach and feasibility analysis
- Risk identification and mitigation planning
Our Approach
Generative AI and agentic systems require architecture decisions that traditional software doesn't — model selection, context design, memory and retrieval strategy, tool access, and evaluation frameworks all need to be defined before a line of code is written. We invest in this upfront so the build phase moves cleanly.
Key Activities:
- Model selection and deployment approach
- RAG and context architecture design
- Agent tool access and memory design
- Evaluation and observability framework
Our Approach
We build and evaluate in parallel — developing the system while continuously testing outputs against real data and business scenarios. Generative AI systems require more rigorous evaluation than traditional software because the outputs are probabilistic, not deterministic. Edge cases and failure modes are tested throughout, not at the end.
Key Activities:
- System and integration development
- Continuous output evaluation and benchmarking
- Prompt engineering and context optimisation
- Failure mode and edge case testing
Our Approach
We deploy with monitoring, logging, and alerting built in from day one. Generative AI behaviour in production often differs from test environments and early observability is what catches issues before they affect users or business outcomes. Staged rollouts reduce go-live risk for customer-facing systems.
Key Activities:
- Production deployment and infrastructure setup
- Monitoring, logging, and alerting configuration
- Performance and latency optimisation
- Staged rollout and go-live support
Our Approach
Generative AI and agentic systems improve with structured feedback loops. We provide ongoing support to refine prompts and context, retrain or fine-tune models as new data becomes available, and expand system capabilities as the business identifies new use cases to address.
Key Activities:
- Prompt and context refinement cycles
- Model fine-tuning and retraining
- New use case scoping and development
- Quarterly system performance reviews
Our Approach
We start by identifying which use cases are genuinely suited to generative AI or agentic approaches, and whether the data and infrastructure required to support them are in place. Most failed AI projects start with the wrong use case or the wrong assumptions about what the data can support.
Key Activities:
- Use case prioritisation and value mapping
- Data availability and quality assessment
- Model approach and feasibility analysis
- Risk identification and mitigation planning
Our Approach
Generative AI and agentic systems require architecture decisions that traditional software doesn't — model selection, context design, memory and retrieval strategy, tool access, and evaluation frameworks all need to be defined before a line of code is written. We invest in this upfront so the build phase moves cleanly.
Key Activities:
- Model selection and deployment approach
- RAG and context architecture design
- Agent tool access and memory design
- Evaluation and observability framework
Our Approach
We build and evaluate in parallel — developing the system while continuously testing outputs against real data and business scenarios. Generative AI systems require more rigorous evaluation than traditional software because the outputs are probabilistic, not deterministic. Edge cases and failure modes are tested throughout, not at the end.
Key Activities:
- System and integration development
- Continuous output evaluation and benchmarking
- Prompt engineering and context optimisation
- Failure mode and edge case testing
Our Approach
We deploy with monitoring, logging, and alerting built in from day one. Generative AI behaviour in production often differs from test environments and early observability is what catches issues before they affect users or business outcomes. Staged rollouts reduce go-live risk for customer-facing systems.
Key Activities:
- Production deployment and infrastructure setup
- Monitoring, logging, and alerting configuration
- Performance and latency optimisation
- Staged rollout and go-live support
Our Approach
Generative AI and agentic systems improve with structured feedback loops. We provide ongoing support to refine prompts and context, retrain or fine-tune models as new data becomes available, and expand system capabilities as the business identifies new use cases to address.
Key Activities:
- Prompt and context refinement cycles
- Model fine-tuning and retraining
- New use case scoping and development
- Quarterly system performance reviews
WHY IT MATTERS
Benefits of Generative AI and Agentic AI
Automation Beyond Rules
Knowledge at Scale
Higher-Quality Outputs
Generative AI accelerates content creation, analysis, and decision support across every function. Teams produce better outputs faster without adding headcount.
Autonomous Execution
Agentic AI handles multi-step tasks across systems with minimal human oversight. Workflows requiring team coordination can run continuously and reliably.
Continuous Improvement
Competitive Advantage
Businesses that deploy generative AI effectively move faster and operate more efficiently. The capability gap between early movers and late adopters compounds quickly.
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
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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 Generative AI and Agentic Systems That Work in Production
From use case to deployment, we deliver it end to end.
