AI & Machine Learning
We build production-grade AI solutions, from conversational agents and recommendation engines to computer vision and predictive analytics, grounded in responsible AI principles.
Efficiency Gains for Clients
ML Models in Production
Average Model Accuracy
What We Deliver
Artificial intelligence is reshaping every industry, but realising its potential requires more than algorithms. Our AI practice bridges the gap between data science experimentation and production-grade systems that deliver measurable business outcomes. We work across the full ML lifecycle, from problem framing and data engineering to model training, deployment, and continuous monitoring.
Our team builds conversational AI agents, demand forecasting models, document intelligence pipelines, and recommendation systems using both classical machine learning and modern large language models. Every solution is designed with explainability, fairness, and governance in mind, ensuring your AI investments are sustainable and trustworthy.
LLM & GenAI Applications
Custom RAG pipelines, fine-tuned models, and AI agents built on our own next gen Connekz Models, OpenAI, XAI, Anthropic, and open-source foundations.
Predictive Analytics
Forecasting models for demand planning, churn prediction, and anomaly detection with quantified confidence intervals.
Document Intelligence
Automated extraction, classification, and summarisation of unstructured documents at enterprise scale.
Responsible AI
Bias auditing, model explainability dashboards, and governance frameworks aligned with emerging AI regulations.
What Sets Us Apart
Intelligent chatbots and voice agents with multi-turn context, intent recognition, and seamless human handoff.
Personalised product, content, and service recommendations using collaborative filtering and deep learning.
Image classification, object detection, and visual inspection systems for manufacturing, retail, and healthcare.
Scalable ETL and feature engineering pipelines that feed clean, enriched data to your ML models in real time.
Automated model retraining, A/B testing, drift detection, and performance monitoring in production environments.
Model risk management frameworks, audit trails, and compliance documentation for regulated industries.
How We Execute
Problem Framing & Data Audit
We define the business problem as a measurable ML task, audit available data sources, and assess feasibility before writing a single line of model code.
Rapid Prototyping
Lightweight proof-of-concept models are trained and evaluated against baseline metrics to validate the approach within weeks.
Production Engineering
Validated models are hardened with proper error handling, monitoring, and scalable serving infrastructure for real-world traffic.
Monitor & Iterate
Continuous monitoring for model drift, automated retraining triggers, and feedback loops that improve accuracy over time.
Real-World Applications
Customer Service AI Agent
A telecommunications provider deployed a conversational AI agent handling 60% of support queries autonomously with 94% customer satisfaction.
Demand Forecasting
A retail chain implemented ML-powered demand forecasting that reduced inventory waste by 25% while maintaining stock availability.
Document Processing Automation
An insurance company automated claims document intake, extracting key fields with 97% accuracy and reducing processing time by 70%.
Predictive Maintenance
A manufacturing firm used sensor data and ML models to predict equipment failures 48 hours in advance, reducing unplanned downtime by 40%.
Ready to Build Your AI & Machine Learning?
Let's turn your vision into a high-performance solution that scales with your ambition.