AI-Augmented Development Services
Most teams add AI to software after it's already built. Classic Informatics delivers AI-augmented software development where intelligence is architected into the product from day one.
Trusted by Enterprises in 30+ Countries
OUR SERVICES
AI-Augmented Development We Deliver
Architecture Consulting
We design systems where AI is a core component, not an afterthought.
Custom Software Development
Custom software built around your business logic, data, and users.
AI Integration & Engineering
We build the pipelines and feedback loops that make AI perform reliably.
Deployment & Optimisation
We manage deployment & optimisation so your product's intelligence improves with use.
OUR SOLUTIONS
Our AI-Augmented Development Solutions
AI-Assisted Development
AI-Powered QA
AI Code Generation
AI-Optimized Delivery Workflow
AI-Assisted Code
Analysis
Development
Efficiency
OUR PROCESS
How We Implement AI-Augmented Development
Our Approach
We begin by understanding the product's goals, user workflows, and the specific decisions or processes where AI should play a role. This is not a generic requirements workshop — it's a targeted session to identify where intelligence will deliver measurable value, and where it will only add complexity. By the end of discovery, you have a product scope with AI use cases defined, prioritised, and grounded in your actual data environment.
Key Activities:
- Map product goals to specific AI use case opportunities
- Assess existing data sources, APIs, and infrastructure for AI readiness
- Define intelligence requirements alongside functional requirements
- Prioritise AI integration points by business value and technical feasibility
- Produce a scoped product brief and AI architecture overview
Our Approach
Good AI-augmented products start with architecture decisions that most teams don't make until it's too late — model selection, data flow design, context management, retrieval strategies, and latency constraints. We design systems where AI operates as a core component, not as a bolted-on module. This phase produces the technical blueprint your engineering and product teams will build from, with clarity on every integration point.
Key Activities:
- Design the application architecture with AI components integrated from the ground up
- Define data pipelines, context flows, and retrieval strategies
- Select models, frameworks, and tooling aligned to your product requirements
- Specify API contracts, latency budgets, and fallback behaviours
- Validate architecture decisions against real-world performance expectations
Our Approach
This is where the product is built. Our engineers develop the application and the AI layer together — not sequentially. Custom models are fine-tuned where needed, integrations are stress-tested at realistic scale, and the product is built with the observability tooling that makes AI behaviour visible in production. We work in structured sprints with regular delivery checkpoints so you have working software at every stage.
Key Activities:
- Build application features alongside AI pipelines in a unified delivery flow
- Fine-tune or configure models against your product's data and context
- Implement retrieval, inference, and feedback loop components
- Set up logging, monitoring, and observability for AI behaviour
- Conduct integration testing across all AI-dependent workflows
Our Approach
Deployment is not the end of the engagement — it's when real performance data becomes available. We manage production deployment, configure performance monitoring, and run a structured stabilisation period to catch and resolve any issues that only surface at scale. We also transfer full technical ownership to your team, with documentation and knowledge sessions that leave your engineers in complete control.
Key Activities:
- Manage production deployment with a structured rollout plan
- Configure performance dashboards and AI behaviour monitoring
- Run a stabilisation period and address production-phase issues
- Complete technical documentation for all AI components and integration points
- Conduct knowledge transfer sessions with your engineering team
Our Approach
AI-augmented software should get better with use, not stagnate at launch quality. We support post-launch optimisation cycles — analysing model performance against real usage, identifying where the product's intelligence is falling short, and iterating on the AI layer as your product and data evolve. This phase is where early AI integrations compound into meaningful competitive advantages.
Key Activities:
- Analyse production AI performance data against baseline benchmarks
- Identify model drift, retrieval gaps, and edge case failures
- Iterate on fine-tuning, prompt engineering, and retrieval strategies
- Expand AI use cases as product usage and data volume grow
- Provide structured recommendations for the next phase of AI capability
Our Approach
We begin by understanding the product's goals, user workflows, and the specific decisions or processes where AI should play a role. This is not a generic requirements workshop — it's a targeted session to identify where intelligence will deliver measurable value, and where it will only add complexity. By the end of discovery, you have a product scope with AI use cases defined, prioritised, and grounded in your actual data environment.
Key Activities:
- Map product goals to specific AI use case opportunities
- Assess existing data sources, APIs, and infrastructure for AI readiness
- Define intelligence requirements alongside functional requirements
- Prioritise AI integration points by business value and technical feasibility
- Produce a scoped product brief and AI architecture overview
Our Approach
Good AI-augmented products start with architecture decisions that most teams don't make until it's too late — model selection, data flow design, context management, retrieval strategies, and latency constraints. We design systems where AI operates as a core component, not as a bolted-on module. This phase produces the technical blueprint your engineering and product teams will build from, with clarity on every integration point.
Key Activities:
- Design the application architecture with AI components integrated from the ground up
- Define data pipelines, context flows, and retrieval strategies
- Select models, frameworks, and tooling aligned to your product requirements
- Specify API contracts, latency budgets, and fallback behaviours
- Validate architecture decisions against real-world performance expectations
Our Approach
This is where the product is built. Our engineers develop the application and the AI layer together — not sequentially. Custom models are fine-tuned where needed, integrations are stress-tested at realistic scale, and the product is built with the observability tooling that makes AI behaviour visible in production. We work in structured sprints with regular delivery checkpoints so you have working software at every stage.
Key Activities:
- Build application features alongside AI pipelines in a unified delivery flow
- Fine-tune or configure models against your product's data and context
- Implement retrieval, inference, and feedback loop components
- Set up logging, monitoring, and observability for AI behaviour
- Conduct integration testing across all AI-dependent workflows
Our Approach
Deployment is not the end of the engagement — it's when real performance data becomes available. We manage production deployment, configure performance monitoring, and run a structured stabilisation period to catch and resolve any issues that only surface at scale. We also transfer full technical ownership to your team, with documentation and knowledge sessions that leave your engineers in complete control.
Key Activities:
- Manage production deployment with a structured rollout plan
- Configure performance dashboards and AI behaviour monitoring
- Run a stabilisation period and address production-phase issues
- Complete technical documentation for all AI components and integration points
- Conduct knowledge transfer sessions with your engineering team
Our Approach
AI-augmented software should get better with use, not stagnate at launch quality. We support post-launch optimisation cycles — analysing model performance against real usage, identifying where the product's intelligence is falling short, and iterating on the AI layer as your product and data evolve. This phase is where early AI integrations compound into meaningful competitive advantages.
Key Activities:
- Analyse production AI performance data against baseline benchmarks
- Identify model drift, retrieval gaps, and edge case failures
- Iterate on fine-tuning, prompt engineering, and retrieval strategies
- Expand AI use cases as product usage and data volume grow
- Provide structured recommendations for the next phase of AI capability
WHY IT MATTERS
Benefits of AI-Augmented Development
Reliable Performance
Faster Time to Value
AI That Improves
Less Manual Work
Product Advantage
Full Engineering Control
Latest Case Studies
-
Insights-driven Online Platform For ASX Listed Companies
Survey-based web & mobile application catering to the financial market research sector.
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Dashboard & BI Application For a Leading Dental Care Provider
We developed a complete Business Intelligence App with Textual and Visual Reporting.
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Integrated Online System That Simplifies Logistics
We offered end-to-end development & integration of an interactive platform for carrier management.
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Interactive Digital Platform For Guided Trip Planning
We built an impressive digital platform to bring travelers and travel experts together, under one platform.
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Centralized Property Management Platform For UK Real Estate Market
We built an all-in-one platform for three type of users- landlords, tenants, and contractors.
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
AI Should Be How Your Product Works. Not an Afterthought.
We build software with intelligence at the core, delivered end to end.
