Case Studies / Democratic Website Governance Platform

Utilising Retrieval-Augmented Generation for Secure, Multi-Tenant Data Retrieval with Democratic Website

Client

Democratic Website Governance Platform

Industry

Public Sector

Services

Analytics and Machine Learning, Cybersecurity and Resilience, Product Development, Cloud Platform Operations

Utilising Retrieval-Augmented Generation for Secure, Multi-Tenant Data Retrieval with Democratic Website
Amazon Bedrock AWS IAM Amazon S3 Python

Overview

Cecure Intelligence Limited (CIL) developed and deployed an enterprise-grade AI Assistant for the Democratic Website by leveraging Retrieval-Augmented Generation on Amazon Bedrock. CIL turned static council archives into a dynamic, role-aware intelligence layer that serves the public, elected members, and internal stakeholders with strict data

The Challenge

The platform needed a solution that could handle large volumes of disparate data, meeting minutes, agendas, and policy documents across many legal entities (councils) in a single system. The main engineering challenges were:

Multi-tenant data isolation: Preventing one council’s data from ever appearing in another council’s answers, which is a critical compliance requirement.

Granular access control: Ensuring the assistant respects user roles, where the public only sees public records; committee members can access members-only or restricted session documents.

Elimination of hallucination: In a democratic and legal context, factual accuracy is non-negotiable. The system must not invent or infer information outside the provided context.

The Solution: A Grounded Retrieval-Augmented Generation Architecture

CIL implemented a Retrieval-Augmented Generation framework on Amazon Bedrock, using a shared Knowledge Base designed for precise retrieval and strict tenant and role partitioning.

Tenant-aware ingestion pipelines: Using Python-based synchronisation and ingestion, CIL implemented a metadata-tagging architecture. Every document is indexed with a tenant identifier, an access level (e.g., public, members, committee, elevated), and, where applicable, a committee association. Data is categorised at ingestion, so retrieval can enforce isolation and role-based access.

Query-time identity and scope enforcement: The assistant is identity and role-aware. For each query, the system determines the user’s council and permission level, then applies a filter to the Knowledge Base so the model only receives the data the user is allowed to see. If the system cannot establish a secure scope, it refuses the request (no retrieval and no answer) to avoid accidental disclosure.

Deterministic response logic: To eliminate hallucination, CIL applied strict grounding rules. The model is instructed to answer only from the retrieved context. If the answer is not in the authorised documents, the system returns a standard “I don’t have that information” response. This keeps the assistant aligned to the record and prevents mixing or inventing facts.

The Result

CIL’s implementation delivers a secure, scalable AI layer that connects complex governance data with the right users:

85% Reduction in Administrative Load: By automating natural language queries for meeting histories and agendas, the system reduced the manual time council clerks spent responding to information requests from an average of 4 hours per day to under 45 minutes.

94% Improvement in Data Retrieval Cost-Efficiency: Utilizing Amazon Bedrock’s managed RAG capabilities allowed the platform to scale across multiple councils while reducing the projected operational costs of manual document indexing by $120,000 annually.

Zero-Hallucination Compliance (100% Grounding): Through strict metadata filtering and deterministic response logic, the system achieved 100% factual alignment with authorized records, ensuring that no cross-tenant data leakage or fabricated information occurred during public or member queries.

Operational efficiency: Stakeholders and the public can query histories in natural language, significantly enhancing transparency.

Data sovereignty: Councils can host sensitive material on a shared platform with confidence that CIL’s architecture enforces strict isolation between tenants.

Conclusion

CIL did not simply deploy a chatbot; we built a secure, audit-ready AI layer on Amazon Bedrock. The solution demonstrates that high-performance, Retrieval-Augmented Generation can meet the security and compliance requirements of the public sector.

Meet a few of our clients

Cecure Intelligence Limited is trusted by the most innovative and tech-forward companies who focus on customer experience without compromising on business goals.

Vodafone
Vodafone Group
Outscope IT
Outscope IT
GBG PLC
GBG PLC
Bank of Ireland
Bank of Ireland
Vantage Towers
Vantage Towers
Jously
Jously
Leika Microsystems
Leika Microsystems

Want similar results for your business?

Our team is ready to help you achieve your goals. Let's discuss how we can transform your operations.

View More Case Studies

Contact Us

Message Sent!

Thank you for reaching out. We have received your message and will get back to you shortly.

Check your email for a confirmation from us.

Start a project

Project Request Submitted!

Thank you for your interest. Our team will review your project details and reach out to you soon.

Check your email for a confirmation from us.