Introduction

A mid-sized government institute with around 800 employees set out to modernize its document-heavy operations through artificial intelligence. Responsible for permits, regulatory oversight, and citizen services, the organization had accumulated decades of unstructured data across legacy systems, including scanned documents, PDFs, and handwritten forms. Facing strict data-protection requirements and rising expectations for faster, more consistent public services, leadership sought to introduce AI capabilities without compromising compliance, security, or data sovereignty.

The Story

Over time, the institute’s information landscape had grown increasingly complex. Critical knowledge was spread across disconnected systems, making it difficult for staff to locate, classify, and reuse documents efficiently. While several AI pilots had been launched to explore document automation and search, these efforts remained isolated and could not scale due to infrastructure and governance limitations.

We partnered with the institute to establish a secure and future-proof foundation for AI adoption. Rather than focusing on individual use cases alone, the engagement aimed to prepare data, infrastructure, and operating models to support a broad range of document intelligence scenarios.

Over a six-month transformation, Kibit worked closely with IT, compliance, and business stakeholders to design an architecture that unified data sources, enabled advanced AI workloads, and met public-sector regulatory standards.

The Challenge

Several interconnected challenges prevented effective AI adoption.

Decades of unstructured data existed in inconsistent formats across multiple systems, with limited metadata and no shared standards. The existing infrastructure was designed for document storage rather than intelligent processing, offering no centralized environment for AI development, deployment, or monitoring.

Uncoordinated AI pilots lacked shared tooling and governance, limiting reuse and learning across teams. Strict public-sector regulations around data protection, transparency, and residency further constrained technology choices. At the same time, staff spent significant time manually searching and classifying documents, leading to inefficiencies, inconsistent responses, and loss of institutional knowledge.

With limited budgets and workforce constraints, any modernization effort needed to deliver measurable efficiency gains without increasing headcount.

The Results

By establishing a secure data and AI foundation, the institute achieved tangible improvements across operations:

Infrastructure modernization: A scalable hybrid architecture reduced AI application delivery timelines from months to weeks.

Data accessibility: Fragmented document archives were unified into a single searchable repository, unlocking decades of institutional knowledge.

Operational efficiency: Automated document processing reduced manual workloads by up to 40%, while AI-powered search and assistants cut document retrieval times by 60%.

Compliance and security assurance: The architecture met strict public-sector requirements and passed external audits with no major findings.

Business value and scalability: The new platform enabled measurable service improvements and created a foundation for future AI initiatives without additional infrastructure investments.

Knowledge preservation: Semantic indexes and knowledge graphs preserved institutional expertise and simplified onboarding for new employees.

The institute is now positioned to expand AI-driven services confidently, balancing innovation with the highest standards of security and regulatory compliance.