Custom AI Product Development & LLM Integration
Custom AI applications, LLM integrations, production-grade MLOps, and AI SaaS platforms — delivered in 8–12 weeks.
AI Product Development is the end-to-end process of designing, building, and deploying custom AI-powered applications — from prototype to production — including LLM integration, MLOps pipelines, API design, and ongoing model operations.
Norvik builds custom AI products that solve specific business problems — not generic chatbots or off-the-shelf tools. We work across the full stack: product discovery, model selection, application development, API design, and production MLOps. Our LLM-agnostic approach means we select the right model — GPT-4, Claude, Gemini, Llama, or a fine-tuned open-source model — based on your requirements, not vendor relationships.
What's included in AI Product Development
Custom AI Applications
End-to-end development of AI-powered applications built to your specifications — from product definition through design, development, testing, and deployment.
AI Integration
Connecting AI capabilities into your existing products, platforms, and workflows via APIs, SDKs, and custom middleware — with minimal disruption to current operations.
MLOps & Deployment
Production-grade model serving, monitoring, drift detection, and retraining pipelines using MLflow, Kubeflow, and cloud-native tooling on AWS and GCP.
AI UX Design
User experience design for AI-powered interfaces — conversation flows, AI feedback patterns, confidence indicators, and human-in-the-loop interaction design.
AI APIs & Platforms
Scalable AI API development with rate limiting, authentication, versioning, and observability — built for internal or external consumption.
AI SaaS Development
Full SaaS product development with AI at the core — multi-tenancy, billing integration, usage metering, and compliance-ready architecture from day one.
AI MVP Development
Rapid MVP delivery in four to six weeks — focused scope, validated assumptions, and a production-quality codebase ready to scale rather than throw away.
Platform Engineering
Developer platforms and internal tools that expose AI capabilities to your engineering teams — accelerating adoption across the organisation.
AI-Powered Portal Development
Client-facing portals with embedded AI — intelligent search, document Q&A, personalised dashboards, and automated reporting built on your proprietary data.
Our delivery process
A proven four-phase methodology that takes you from first conversation to production AI — with full accountability at every step.
Discover
Product discovery sprint: requirements gathering, technical feasibility assessment, model selection, and architecture design. Output: approved technical spec.
Design
UX design, data pipeline design, API contract definition, and infrastructure architecture. All decisions validated before a line of application code is written.
Build
Iterative development in two-week sprints with working software at every checkpoint. Model integration, API development, frontend, and MLOps pipeline built in parallel tracks.
Deploy
Production deployment with monitoring, alerting, model drift detection, and runbook documentation. Handover includes full source code, architecture docs, and 30-day hypercare.
Discover
Product discovery sprint: requirements gathering, technical feasibility assessment, model selection, and architecture design. Output: approved technical spec.
Design
UX design, data pipeline design, API contract definition, and infrastructure architecture. All decisions validated before a line of application code is written.
Build
Iterative development in two-week sprints with working software at every checkpoint. Model integration, API development, frontend, and MLOps pipeline built in parallel tracks.
Deploy
Production deployment with monitoring, alerting, model drift detection, and runbook documentation. Handover includes full source code, architecture docs, and 30-day hypercare.
Discover
Product discovery sprint: requirements gathering, technical feasibility assessment, model selection, and architecture design. Output: approved technical spec.
Design
UX design, data pipeline design, API contract definition, and infrastructure architecture. All decisions validated before a line of application code is written.
Build
Iterative development in two-week sprints with working software at every checkpoint. Model integration, API development, frontend, and MLOps pipeline built in parallel tracks.
Deploy
Production deployment with monitoring, alerting, model drift detection, and runbook documentation. Handover includes full source code, architecture docs, and 30-day hypercare.
Technology-agnostic.Outcome-obsessed.
We select tools based on your requirements, not vendor relationships.
LLM
- OpenAI GPT-4
- Anthropic Claude
- Google Gemini
- Meta Llama 3
Framework
- LangChain
- LlamaIndex
- PyTorch
- Hugging Face
Backend
- FastAPI
- Python
Frontend
- React
- Next.js
MLOps
- MLflow
- Kubeflow
Cloud
- AWS
- GCP
Common questions about AI Product Development
Straight answers to the questions we hear most often.
Still have questions? Talk to our team
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