AI / ML Development

What We Do

Machine learning is powerful when applied to the right problems with the right data and underwhelming when it is not. We make sure the distinction is clear before we start. Our team covers the full AI/ML lifecycle from data preparation and model development through to deployment, monitoring, and ongoing optimisation across supervised learning, deep learning, NLP, computer vision, and Generative AI.

We do not overstate model capability or understate the work involved in getting data ready. What we deliver is AI that performs in the context of your actual business.

Our AI / ML Services

AI/ML Consulting & Integration

We identify viable use cases, assess your data readiness, recommend the right architecture, and integrate completed AI/ML solutions into your existing applications and workflows.

Use case identification and feasibility assessment

Data readiness and infrastructure review

Model selection and architecture design

API-based model serving and enterprise system integration

Intelligent Automation

ML-powered automation that makes context-aware decisions not rigid rules that break on edge cases. We reduce manual workload, improve consistency, and scale as your operation grows.

ML-powered document processing and data extraction

Intelligent workflow routing and classification

Automated quality control and anomaly detection

Business process automation with adaptive learning

LLM Integration GPT-4, Claude, Gemini

We integrate Large Language Models into your applications building content generation pipelines, document summarisation systems, intelligent search, and RAG systems grounded in your own data.

GPT-4 and Claude integration into existing applications

RAG systems for knowledge-grounded LLM responses

Document summarisation and content generation pipelines

Prompt engineering and output quality optimisation

Predictive Analytics & Forecasting

Predictive models that forecast future outcomes demand, churn, equipment failure, price movements, and customer lifetime value so your team acts ahead of problems, not reactively.

Demand and sales forecasting

Customer churn prediction and retention scoring

Equipment failure prediction and predictive maintenance

Financial risk and credit scoring models

AI-Based IoT Solutions

We connect industrial machines and IoT sensors with AI analytics that detect anomalies, forecast failures, and trigger automated responses reducing unplanned downtime and generating measurable operational savings.

IoT sensor data ingestion and real-time processing

Predictive maintenance using sensor-based ML models

Anomaly detection in manufacturing and industrial environments

Integration with AWS IoT, Azure IoT Hub, and similar platforms

Self-Learning Analytics Tools

Analytics systems that continuously learn from new data refining predictions over time, updating forecasts as conditions change, and becoming more useful the longer they run.

Adaptive recommendation engines

Self-updating forecasting models with drift detection

Automated retraining pipelines

Behaviour-based personalisation systems

AI/ML Mobile App Development

iOS and Android apps with embedded ML on-device inference for real-time features, cloud ML for heavier processing, and AI-powered recommendations, image recognition, and NLP-driven interfaces.

On-device ML using TensorFlow Lite and Core ML

AI-powered personalised recommendations

Image recognition and computer vision in mobile apps

NLP-driven search and conversational interfaces

Data Support for AI/ML

Poor data preparation is the most common reason AI/ML projects underperform in production. We treat data quality as a first-class concern collection, cleaning, labelling, annotation, and pipeline engineering.

Data cleaning, deduplication, and normalisation

Data labelling and annotation for supervised learning

Feature engineering and transformation pipelines

Data versioning and lineage tracking with DVC

Core AI/ML Capabilities

Machine Learning Models

Supervised and unsupervised models for classification, regression, clustering, and anomaly detection selected based on your data and requirements, not what's fashionable.

Deep Learning

CNNs for image and visual data, RNNs and LSTMs for sequential data, Transformers for language and multimodal tasks.

Natural Language Processing (NLP)

Text classification, entity extraction, sentiment analysis, summarisation, and conversational interfaces using Hugging Face, spaCy, BERT, and LLM-based approaches.

Computer Vision

Object detection, image classification, OCR, visual quality monitoring for manufacturing QC, medical imaging, document processing, and visual search.

Generative AI & LLMs

GPT-4, Claude, Gemini, and open-source LLMs for content generation, summarisation, search, and conversational AI with LangChain and LlamaIndex for RAG and agent architectures.

MLOps

Model versioning with MLflow and DVC, automated pipelines with Airflow, model serving with Docker and Kubernetes, and performance monitoring with Evidently AI.

AIOps

AI applied to IT operations monitoring infrastructure, predicting failures, automating incident response, and reducing downtime.

Technology Stack

Languages

Python • R • Java • C++ • Scala • Go

Deep Learning

TensorFlow • PyTorch • Keras • Hugging Face Transformers • FastAI

ML Libraries

Scikit-learn • XGBoost • LightGBM • CatBoost • H2O.ai

LLM & GenAI

LangChain • LlamaIndex • OpenAI APIs • FAISS • Pinecone • Weaviate

Data Engineering

Apache Spark • Kafka • Airflow • Pandas • NumPy • Dask

Cloud Platforms

AWS SageMaker • Google Vertex AI • Azure ML • Databricks • Snowflake

MLOps & Monitoring

MLflow • DVC • Weights & Biases • Evidently AI • Docker • Kubernetes

Industries We Serve

Healthcare

Drug discovery assistance, clinical image analysis, patient diagnosis support, clinical workflow optimisation, and health records management.

Finance & Fintech

Fraud detection, algorithmic trading support, credit scoring, personalised financial planning, and regulatory compliance monitoring.

Retail & eCommerce

Product recommendation engines, demand forecasting, customer behaviour analysis, dynamic pricing, and AI-powered customer support.

Manufacturing & Logistics

Predictive maintenance, computer vision quality control, supply chain optimisation, production planning, and defect detection.

Insurance

Automated claims processing, personalised risk assessment, fraud detection, and predictive risk modelling.

Education

AI tutoring assistants, assessment generation, administrative automation, and adaptive learning platforms.

How We Work

We assess your data landscape and identify AI/ML opportunities worth pursuing honestly, including where the data doesn’t support a particular application.

We collect, analyse, and prepare your data assessing quality, identifying gaps, engineering features, and designing training-ready pipelines.

We build, train, test, and iterate splitting data correctly, measuring the right metrics, and validating on held-out data the model has never seen.

We deploy to production and set up monitoring to track performance, detect drift, and alert when the model needs attention.

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Why Web Chip Armor

Here is what genuinely distinguishes our React team.

Honest about what the data supports

We assess your data at the outset and tell you what’s achievable before committing to a build. If the data doesn’t support a particular AI application, we say so.

Production-grade systems, not prototypes

We build for real-world data volumes, request rates, and usage patterns not demo conditions. Prototype performance doesn’t always translate to production; we make sure it does.

Full-stack AI and software capability

Data engineering, model development, API integration, frontend, and cloud deployment we cover the full stack so nothing falls between teams.

LLM and advanced AI depth

Genuine expertise in LLM development, RAG architecture, fine-tuning, and prompt engineering built on real production deployments, not theoretical knowledge.

Agile delivery with regular visibility

Structured sprints, clear deliverables, and working outputs at every stage. You never receive a large untested system at the end of a long engagement.

NDA signed before any project discussion

Your business requirements, data, and all project information remain strictly confidential.

Frequently Asked Questions

How much data do we need to build a machine learning model?

It depends on the complexity of the task. Simple classification or regression models can be effective with a few thousand labelled examples. Deep learning typically needs tens of thousands or more. We assess your data at the outset and advise on whether it’s sufficient, what additional collection is needed, and whether transfer learning could bridge the gap.

Can you implement AI/ML into our existing mobile or web applications?

Yes. Integrating AI/ML into existing applications is a core part of what we do. We review your architecture, available data, and performance requirements before recommending an approach then build and integrate the AI/ML components as embedded on-device models, cloud API calls, or batch processing pipelines.

What is the difference between AI, ML, and deep learning?

AI is the broad field of building systems that exhibit intelligent behaviour. Machine learning is a subset of AI systems that learn from data rather than following explicit rules. Deep learning is a subset of ML using multi-layer neural networks, which excels at complex tasks like image recognition and language understanding when sufficient data is available.

How do you ensure data security during development?

Secure environments with access controls and encryption in transit and at rest. NDAs before any project begins. Privacy-by-design principles across every data pipeline. For sensitive data, we recommend architectures that minimise exposure on-premise processing or enterprise-grade cloud AI services with contractual data privacy guarantees.

How long does an AI/ML project take?

A focused model such as a churn prediction system or document classifier can be delivered in six to ten weeks. A more complex system involving data engineering, multiple models, and enterprise integration typically takes three to six months. We provide a realistic estimate after the discovery phase.

Do you provide post-deployment support?

Yes. Monitoring model performance, detecting and correcting drift, retraining on new data, resolving issues, and extending capabilities as requirements evolve. AI systems that are not actively maintained degrade we prevent that.

Ready to Start Your AI/ML Project?

Whether you have a specific use case in mind, a data asset you want to make more useful, or a business problem you’re wondering whether machine learning could help with we’d like to hear about it. We’ll give you an honest assessment of what’s achievable and what it would involve.