AI and Machine Learning integrated into existing web and software systems

The focus is on applied implementations that are production-ready, maintainable, and aligned with established engineering practices.

text

 

cms-hub

AI Scoping and Technical Assessment

Clear direction before implementation.

Support for defining scope, feasibility, and delivery approach for AI and ML initiatives. Work is grounded in technical constraints, data readiness, and integration requirements.

  • Technical feasibility assessment
  • AI capability scoping
  • Delivery planning and prioritisation
cms-hub

AI and ML System Integration

Embedding AI into live platforms.

Integration of AI and ML components into existing applications and services using API-driven and service-oriented approaches.

  • Model integration into applications
  • System and workflow integration
  • Scalable inference setup
cms-hub

Deep Learning Engineering

Neural network-based models for complex data problems.

Design and implementation of deep learning models where advanced pattern recognition or representation learning is required.

  • Model architecture design
  • Training and evaluation
  • Performance optimisation
cms-hub

AI Chatbot Development

Conversational systems integrated into digital platforms.

Development of AI-driven chat and conversational interfaces designed to integrate with existing applications, services, and data sources.

  • Conversational flow design
  • Backend and API integration
  • Deployment within web applications
cms-hub

AI Ops and ML Ops

Operational management of AI systems.

Implementation of processes and tooling to support deployment, monitoring, versioning, and reliability of AI and ML models in production.

  • Model deployment pipelines
  • Monitoring and performance tracking
  • Lifecycle and version management
cms-hub

Natural Language Model Engineering

Language-focused AI models.

Development of NLP models for text analysis, classification, and language-based application features.

  • Text processing and feature extraction
  • Model training and validation
  • Integration into applications
cms-hub

AI System Support and Maintenance

Ongoing support for deployed AI systems.

Support for monitoring, optimisation, and incremental improvement of AI and ML components in live environments.

  • Performance monitoring
  • Issue resolution
  • Model updates and enhancements
cms-hub

Working With Engineering Teams

  • Delivery-focused collaboration.
  • Senior, production-focused engineers
  • Clear technical communication
  • Experience with modern web and software stacks

Work is delivered as part of existing teams and workflows, with a focus on predictable outcomes and long-term maintainability.

Tech Stack

openai
python-logo
GoogleCloudAI
Pandas
TensorFlow