AI Integration: Streamlining Accreditation Workflow in Education

NIX helped a US-based legal service provider automate and accelerate accreditation reporting for universities with an AI-powered web platform.

Business Domain

Education
Service

Web Development, Data Science, Cloud Services, Generative AI, AWS
Technologies

AWS, Prompt Engineering, RAG

Business Overview

Our client is a U.S.-based legal service provider that assists educational institutions in navigating the complex accreditation process. To gain accreditation, universities must submit extensive documentation and written explanations called “narratives” that demonstrate how they meet specific academic standards. This task is often manual, time-consuming, and prone to inconsistencies.

The client had already developed an internal web platform to manage documentation workflows. However, they lacked the technical expertise to embed AI functionality into the system. To automate the generation of narratives based on evidence files provided by universities, they turned to NIX as a trusted tech partner with experience in delivering scalable AI-powered solutions.

Project Scope

Our role was to bring AI and ML capabilities to the platform, streamlining the document preparation process and enhancing consistency in accreditation reports.

Challenge

Our team had to design a scalable architecture capable of handling varied document types and multiple accreditation formats, all while meeting regulatory and operational demands.
Since generative models can produce non-deterministic results that may be unsuitable for regulated processes like accreditation, we paid special attention to fine-tuning the AI results. Our goal was to ensure consistency, traceability, and compliance with academic standards.

Solution

To enhance the existing system with new capabilities, NIX experts delivered AI-powered functionality that automates accreditation narrative generation and streamlines every step of the workflow.

To this end, our team designed a robust AI layer built on AWS, aligning seamlessly with the client’s current cloud infrastructure. The chosen approach ensured smooth integration while taking full advantage of AWS’s scalability, security, and high availability.

Key Features

AI components were seamlessly integrated into the client’s existing web platform. As a result, the platform now delivers a range of features that run on the AI layer, automating document processing and reducing the need for manual effort.

Smart document upload and processing

Users can upload PDF, Word, and Excel files containing institutional data. The system automatically processes the content and stores it securely for analysis.
Built-in standards navigator

Users choose the right option from an integrated library of accreditation standards, each with clear descriptions and instructions—making it easy to target the right criteria.
AI-generated, evidence-cited narratives

With a single click, users receive structured narratives backed by automatically cited references from uploaded files—ensuring fast, credible, and standard-aligned outputs.
Live editor for final touches

Users can review, adjust, and approve AI-generated content within an intuitive editor, streamlining the revision process.
Export-ready reports

Final narratives can be exported and inserted into accreditation reports with minimal manual formatting.
Full edit history and compliance logs

The system logs every version and edit made to each narrative, ensuring full visibility into what was changed—as well as when and by whom—for audit and compliance purposes.

AI Technologies and Architecture

LLMs via AWS Bedrock generate accreditation narratives, transforming raw evidence into structured, standard-compliant text.
Retrieval-augmented generation (RAG) integration combines document retrieval with LLM-based generation to deliver accurate, context-aware outputs.
Semantic chunking and embedding break documents into meaningful segments and convert them into vector embeddings to build a searchable knowledge base.
Amazon Aurora with PGVector stores the knowledge base and metadata securely while enabling fast semantic search to retrieve the most relevant document chunks.
AWS Textract extracts text from various document formats to ensure that all relevant information is captured for further processing.

SageMaker with a secondary LLM enables the creation and debugging of preprocessing pipelines and performs automated evaluation of generated narratives.
LangChain orchestrates the AI workflow, managing interactions between components, constructing prompts, and maintaining data flow.
AWS Lambda powers backend logic, processing documents, handling user requests, and triggering AI inference in a modular, serverless environment.
Amazon S3 stores original documents and generated outputs securely, ensuring reliable access and traceability.
boto3 SDK connects all AWS services, enabling seamless communication and integration across the entire platform.

Outcome

The client’s upgraded platform features robust AI capabilities that drastically reduce the time and effort required for narrative generation—from hours of manual work to just a few minutes per standard.

With NIX’s contribution, the client gained:

A streamlined process for document handling and AI-driven narrative creation
Reliable, high-quality outputs aligned with accreditation expectations
Transparent, evidence-linked narratives for full traceability

The system’s scalable design ensures it can accommodate new accreditation frameworks and growing documentation volumes as the client expands their offerings.

Our partnership with the client continues. Upcoming development phases include:

AI-based analysis of video and image files to expand the evidence base
Automated chart and graph generation to visualize key metrics
Enhanced narrative generation using domain-specific compliance models

Success Metrics

90%

Reduction in Narrative Preparation Time:

From hours to minutes per standard, greatly improving operational efficiency.

Faster Report Compilation:

Thanks to integrated AI processing and export-ready formats.

100%

Citation Traceability:

Every narrative output includes verifiable references to source files.

80%

Decrease in Manual Editing:

Due to the high initial accuracy of AI-generated drafts.

Team:

Project Manager Solution Architect Data Scientist

Tech stack:

AWS Prompt Engineering Vector Databases Langchain boto3 RAG AWS Textract EC2 SageMaker Lambda RDS pgvector S3 Fargate

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