Technical Services

From analytical concept to production-ready software

Analytical Factory provides the technical expertise required to take analytical ideas from concept through to operational software.

Our capabilities span data, analytics, artificial intelligence, software engineering and deployment. This means we can contribute at any stage of an analytical project — from developing a new analytical approach to engineering a complete application around it.

Data and analytical engineering; effective analytics starts with effective data.

We develop software and data-processing capabilities for working with complex, heterogeneous and high-volume data sources. This can include structured datasets, databases, documents, text, customer feedback, voice and other unstructured information. Our objective is to create reliable foundations on which analytical models and applications can operate and we can encompass:

Data ingestion and transformation
Data preparation and processing
Integration with existing data sources and systems
Feature engineering and analytical data preparation
Structured and unstructured data processing
Automated analytical workflows
Data pipelines supporting analytical applications

Statistical and machine learning development

We develop analytical solutions using the techniques most appropriate to the problem. These can range from established statistical methods through to more advanced machine learning approaches. Where appropriate, we combine multiple analytical techniques within a single application rather than treating individual models as isolated components. Techniques include:

Predictive modelling
Classification
Segmentation
Decision trees
Regression and statistical modelling
Pattern recognition
Natural language processing
Supervised and unsupervised learning
Model-based analytical workflows

Unstructured data and language analytics

A significant proportion of organisational data is unstructured. Customer comments, survey responses, documents, emails, transcripts and other textual sources can contain valuable information that is difficult to analyse using conventional structured-data techniques. Where appropriate, these capabilities can combine conventional natural language processing, machine learning and modern generative AI techniques. We develop applications capable of processing these sources to perform tasks such as:

Classification and categorisation
Thematic analysis
Information extraction
Entity and concept identification
Codification
Sentiment and other linguistic analysis
Summarisation
Search and retrieval
Automated processing of large volumes of text

Generative AI and intelligent applications

Generative AI provides powerful new capabilities for analytical applications, but it also introduces important questions around accuracy, governance, security, cost and reliability. We therefore approach generative AI as an engineering capability rather than a solution in itself. Where generative AI is appropriate, we engineer it into the wider application architecture with appropriate controls, rather than treating the underlying model as the complete solution. Depending on the application, we can incorporate technologies and techniques including:

Large language models
Retrieval-Augmented Generation (RAG)
Semantic search and vector-based retrieval
AI-assisted classification and extraction
Automated summarisation
Natural-language interfaces
Agentic AI workflows
Hybrid AI and conventional analytical approaches

Voice and speech analytics

Voice data represents a rich but often underused source of information. We develop analytical workflows that can transform spoken interactions into data suitable for subsequent analysis. These can include:

Voice → transcription → translation → analysis → insight

Applications can therefore incorporate automated speech recognition, translation and downstream analytical processing to extract structured and unstructured insight from spoken interactions. This can support the analysis of customer conversations, interviews, operational communications and other sources of recorded speech.

Model deployment and management

Developing a model is only part of the process. We provide technical capabilities for taking data science models out of development environments and incorporating them into operational software. This can include:

Model packaging and deployment
Model serving
Integration with applications and data workflows
Model version management
Monitoring and performance tracking
Automated processing pipelines
Batch and real-time analytical workflows
Operational integration