AI Agent Development
Your teams are doing work that AI agents can handle easily. We build and deploy AI agents that act, decide, and deliver.
Trusted by Enterprises in 30+ Countries
OUR SERVICES
AI Agent Development Services We Deliver
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Agentic Workflow Automation
We build agentic AI systems that handle multi-step workflows — reasoning, deciding, and acting without waiting for human input.
Document
Processing
We build agents that handle document processing at scale — extracting, classifying, and acting on unstructured content without manual review.
Multi-Agent
Orchestration
We design multi-agent systems where specialized AI agents collaborate, delegate, and hand off tasks across your operations.
OUR SOLUTIONS
Our Agentic AI Solutions
Insurance Claims Intake Agents
Medical Records & Prior Auth Agents
Production Monitoring
Agents
Customer Support
Agents
Internal Workflow Orchestration Agents
Compliance Monitoring
Agents
OUR PROCESS
How We Implement AI Agents
Our Approach
We identify which processes are genuinely worth automating — mapping volume, variability, and business impact before scoping the agent's objectives, decision authority, and boundary conditions. Nothing gets built until the right use case is confirmed.
Key Activities:
- Process mapping and automation opportunity assessment
- Agent objective and decision scope definition
- Integration surface and data access audit
- Success metric and ROI baseline setting
Our Approach
We design the agent's reasoning structure, tool connections, and escalation logic based on your specific workflow. Every intelligent agent has defined boundaries — how it handles ambiguity, when it escalates, and how every decision gets logged.
Key Activities:
- Agent reasoning and task planning design
- Tool and API integration mapping
- Escalation and human-in-the-loop logic design
- Memory and context management strategy
Our Approach
Our custom AI agent development process connects agents to your actual systems in structured phases, validating each integration point before moving forward. AI agents for enterprise must work reliably in real environments — not just controlled sandboxes.
Key Activities:
- Agent development and tool integration
- LLM and model selection and configuration
- CRM, ERP, and internal system connectivity
- Authentication and access control implementation
Our Approach
We put agents through adversarial testing — edge cases, unexpected inputs, data gaps — before they touch production workflows. Every agent is validated against real business scenarios, not synthetic benchmarks, so failures surface before go-live.
Key Activities:
- Functional and edge-case testing
- Accuracy and decision quality validation
- Failure mode and fallback testing
- Human review and audit trail verification
Our Approach
We deploy agents with full observability in place — monitoring, alerting, and audit logging set up before go-live. Your team gets complete ownership and visibility into every decision the agent makes from day one.
Key Activities:
- Production deployment and rollout management
- Agent monitoring and observability setup
- Performance tuning and feedback loop integration
- Engineering team knowledge transfer and documentation
Our Approach
We identify which processes are genuinely worth automating — mapping volume, variability, and business impact before scoping the agent's objectives, decision authority, and boundary conditions. Nothing gets built until the right use case is confirmed.
Key Activities:
- Process mapping and automation opportunity assessment
- Agent objective and decision scope definition
- Integration surface and data access audit
- Success metric and ROI baseline setting
Our Approach
We design the agent's reasoning structure, tool connections, and escalation logic based on your specific workflow. Every intelligent agent has defined boundaries — how it handles ambiguity, when it escalates, and how every decision gets logged.
Key Activities:
- Agent reasoning and task planning design
- Tool and API integration mapping
- Escalation and human-in-the-loop logic design
- Memory and context management strategy
Our Approach
Our custom AI agent development process connects agents to your actual systems in structured phases, validating each integration point before moving forward. AI agents for enterprise must work reliably in real environments — not just controlled sandboxes.
Key Activities:
- Agent development and tool integration
- LLM and model selection and configuration
- CRM, ERP, and internal system connectivity
- Authentication and access control implementation
Our Approach
We put agents through adversarial testing — edge cases, unexpected inputs, data gaps — before they touch production workflows. Every agent is validated against real business scenarios, not synthetic benchmarks, so failures surface before go-live.
Key Activities:
- Functional and edge-case testing
- Accuracy and decision quality validation
- Failure mode and fallback testing
- Human review and audit trail verification
Our Approach
We deploy agents with full observability in place — monitoring, alerting, and audit logging set up before go-live. Your team gets complete ownership and visibility into every decision the agent makes from day one.
Key Activities:
- Production deployment and rollout management
- Agent monitoring and observability setup
- Performance tuning and feedback loop integration
- Engineering team knowledge transfer and documentation
WHY IT MATTERS
Benefits of AI Agents
Work That Runs Overnight
Fewer Manual Handoffs
Consistent Decisions
Scales Without Headcount
Faster Operations
Lower Operational Risk
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
Ready for AI Agents That Actually Run Your Workflows?
We scope, build, and deploy — end to end.
