Turn everyday interactions into actionable insight.

Aurista is IMPOWER’s AI-enabled insights platform designed to analyse large volumes of unstructured data, including case notes, assessments, call transcripts, emails, surveys and other digital interactions. Using a combination of Generative AI, Natural Language Processing (NLP), and data science techniques, Aurista identifies themes, sentiment, risk indicators, patterns and opportunities that may not be visible through traditional reporting methods.

Purpose
The platform helps organisations unlock value from existing data sources by transforming text-based information into actionable intelligence. This enables leaders and operational teams to better understand service demand, quality, outcomes, risks and opportunities for improvement.

Functionality
Aurista provides a range of analytical capabilities, including:
• Identification of emerging themes and trends.
• Sentiment analysis to understand experiences and perceptions.
• Detection of potential risks and safeguarding concerns.
• Recognition of inconsistent practice or variation in service delivery.
• Exploration of relationships between themes and outcomes.
• Production of dashboards, summaries and reports to support decision-making.
• Analysis of large datasets significantly faster than manual review methods.

Typical Use Cases
Aurista is particularly suited to sectors managing large volumes of text-based records, including:
• Adult Social Care
• Children’s Services
• Health and Care Systems
• Local Government services
• Customer and support service environments

Information Governance
Aurista is a secure and compliant insights platform, designed to enable organisations to safely analyse existing operational data.
Access is role-based and restricted to authorised users, with clear accountability for data ownership, storage, processing, audit and oversight.

Data Requirements
Aurista works best where organisations hold significant volumes of text-based information. Typical data sources include:
• Case management systems.
• Children’s services assessments.
• Contact centre transcripts.
• Survey responses and feedback.
• Email correspondence.

Data Volumes
Aurista is designed specifically to address datasets that are too large or complex for manual review. The platform can process and analyse thousands of records simultaneously, allowing identification of patterns across whole populations rather than relying on sampling exercises. This enables broader coverage, faster analysis and more comprehensive insight generation than traditional audit approaches.

AI Analysis Process
A typical Aurista workflow involves:
1. Data Ingestion – Relevant datasets are securely extracted from operational systems.
2. Data Preparation – Information is cleaned, structured and prepared for analysis.
3. Natural Language Processing – AI models review free-text content to identify keywords, concepts, themes and relationships.
4. Pattern Recognition – The platform detects recurring trends, sentiment, behaviours and risk indicators.
5. Insight Generation – Findings are grouped into meaningful categories and presented through visualisations, summaries and reports.
6. Human Review – Practitioners and leaders interpret outputs, validate conclusions and determine actions.
7. Continuous Learning – Analysis can be repeated periodically to monitor trends and measure the impact of interventions.

ICT
IMPOWER works with Crozier Scott Ltd as a sub-processor for managing and developing the Aurista platform, utilising AWS storage, which is their tool for extracting insights from unstructured data. Crozier Scott have certificates of compliance with recognised standards, notably through Cyber Essentials and the NHS Data Security and Protection (DSP) Toolkit submission. All hosting infrastructure holds ISO 27001 accreditation.

Key Benefits
• Faster analysis of large-scale datasets.
• Improved understanding of service-user experiences.
• Earlier identification of emerging risks.
• Evidence-based service improvement.
• Enhanced quality assurance and consistency.
• Better targeting of resources and interventions.
• Increased organisational learning from existing data assets