AI Integration Services
You don't always need to rebuild to add AI. We integrate intelligence into your existing products and platforms so they deliver more without starting over.
Classic Informatics built and modernized our loan workflow platform, maintaining the same team across three years — critical for a product of this complexity.
Soren Scheibye, Co-Founder, UdenomBanken,
Rated 4.9/5 Stars on Clutch
Rated 4.9/5 on Goodfirms
Trusted by Enterprises in 30+ Countries
OUR SERVICES
AI Integration Services We Deliver
AI Integration Consulting
We assess your product, identify the right integration points, and define a practical approach before any build begins.
AI Feature Integration
We integrate AI capabilities directly into existing product architecture without disrupting live functionality.
LLM and Generative AI Integration
We design and build the context layer, prompt architecture, and guardrails that make LLM integration production-ready.
AI API and Third-Party Integration
We integrate AI APIs and third-party models with your platforms so data flows reliably between them.
OUR SOLUTIONS
Our AI Integration Solutions
AI-Powered Search
In-Product AI Assistants
.svg)
Personalisation Engines
Products serving the same experience to every user leave engagement and revenue uncaptured. We integrate AI personalisation that adapts content, recommendations, and flows per user.
Intelligent Data Extraction
Platforms handling documents and unstructured data manually create bottlenecks. We integrate AI extraction layers that read, classify, and process incoming data automatically.
Predictive Features
AI Content Generation
OUR PROCESS
How We Implement AI Integration
Our Approach
We start by understanding the product or platform, the AI capability you want to add, and what the integration needs to connect to. Getting clarity on the use case, the data available, and the technical constraints before building prevents the misaligned integrations that get pulled after launch.
Key Activities:
- Product and platform architecture review
- AI use case definition and value mapping
- Data availability and quality assessment
- Integration approach and risk assessment
Our Approach
We design the integration architecture — the data flows, context management, API layers, and fallback logic required to make AI work reliably within the existing system. Good integration design accounts for latency, error handling, and the impact on existing product performance before a line of code is written.
Key Activities:
- Integration architecture and API design
- Context and data pipeline design
- Prompt and model configuration
- Error handling and fallback logic
Our Approach
We build the integration in phases, testing AI outputs against real product data and user scenarios throughout. AI integrations require more careful testing than standard features because model behaviour is variable — we validate accuracy, latency, and edge cases continuously rather than at the end.
Key Activities:
- Integration development and feature build
- AI output testing and accuracy validation
- Performance and latency testing
- User acceptance testing with real workflows
Our Approach
We deploy AI integrations with monitoring built in from day one, using staged rollouts to validate performance with real users before full release. AI behaviour in production can differ from test environments — early observability is what catches issues before they affect users at scale.
Key Activities:
- Staged deployment and rollout management
- Performance and output monitoring setup
- Issue triage and resolution support
- Post-launch accuracy and latency review
Our Approach
AI integrations improve with use and with feedback. We provide ongoing support to refine prompts and context, improve model accuracy, and expand AI capabilities as the product evolves and new use cases become clear.
Key Activities:
- Prompt and context optimisation
- Model accuracy improvement and tuning
- New AI feature scoping and development
- Quarterly integration performance reviews
Our Approach
We start by understanding the product or platform, the AI capability you want to add, and what the integration needs to connect to. Getting clarity on the use case, the data available, and the technical constraints before building prevents the misaligned integrations that get pulled after launch.
Key Activities:
- Product and platform architecture review
- AI use case definition and value mapping
- Data availability and quality assessment
- Integration approach and risk assessment
Our Approach
We design the integration architecture — the data flows, context management, API layers, and fallback logic required to make AI work reliably within the existing system. Good integration design accounts for latency, error handling, and the impact on existing product performance before a line of code is written.
Key Activities:
- Integration architecture and API design
- Context and data pipeline design
- Prompt and model configuration
- Error handling and fallback logic
Our Approach
We build the integration in phases, testing AI outputs against real product data and user scenarios throughout. AI integrations require more careful testing than standard features because model behaviour is variable — we validate accuracy, latency, and edge cases continuously rather than at the end.
Key Activities:
- Integration development and feature build
- AI output testing and accuracy validation
- Performance and latency testing
- User acceptance testing with real workflows
Our Approach
We deploy AI integrations with monitoring built in from day one, using staged rollouts to validate performance with real users before full release. AI behaviour in production can differ from test environments — early observability is what catches issues before they affect users at scale.
Key Activities:
- Staged deployment and rollout management
- Performance and output monitoring setup
- Issue triage and resolution support
- Post-launch accuracy and latency review
Our Approach
AI integrations improve with use and with feedback. We provide ongoing support to refine prompts and context, improve model accuracy, and expand AI capabilities as the product evolves and new use cases become clear.
Key Activities:
- Prompt and context optimisation
- Model accuracy improvement and tuning
- New AI feature scoping and development
- Quarterly integration performance reviews
WHY IT MATTERS
Benefits of AI Integration
No Rebuild Required
Faster Time to Value
Lower Risk
Adding AI to a stable product carries far less delivery risk than a full rebuild. Existing functionality stays intact while new capabilities are layered in incrementally.
Competitive Feature
Products without AI features lose ground to competitors shipping intelligent capabilities. Integration closes that gap without a major re-architecture investment.
Better User Experiences
Data Already in Place
Existing products already hold the user and operational data that makes AI valuable. Integration puts that data to work rather than waiting to accumulate it in a new system.
Latest Case Studies
-
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 → -
.png)
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
.png)
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
Add AI to Your Product Without Starting Over
From integration assessment to deployment, we handle it end to end.
