Case Study — Pension Tech.
Accelerating 75%
Faster Pension SRS Creation
with AI-Driven Extraction
Automated SRS generation for a leading Pension Tech
platform, transforming manual documentation into an AI-driven
process with 100% traceability and zero missed validations.
Success Highlights
Key Details
Industry: Pension Tech / Financial Services Platform Type: SaaS-based Pension Administration
Technology Stack: JavaScript, AI Models, NLP Processing, Automated Documentation Frameworks
Business Challenge
The client, a leading SaaS provider for pension administration, needed to maintain accurate and auditable requirement documentation across multiple JavaScript modules. With business logic scattered and documentation done manually, teams struggled to align code behavior with compliance-driven SRS standards.
Teams used varied structures, reducing audit readiness and standardization.
Our Solution Approach
We designed and implemented an AI-driven SRS generation framework to automate extraction, structuring, and validation of business requirements from JavaScript files.
1 · Discover
Intelligent Code Analysis
Fed multiple JS modules (new.js, load.js, save.js, delete.js) into an AI model trained to parse business logic and validation patterns.
The system automatically identified dependencies, validation flows, and event triggers.
2 · Automate
AI-Powered Requirement Extraction
Used natural language prompts to guide the AI model in extracting complete functional requirements from all modules.
The AI segmented results into submodules (New, Load, Save, Delete) with contextual accuracy and zero manual intervention.
3 · Structure
Standardized SRS Generation
Generated standardized six-part SRS documents encompassing functional, data, and non-functional requirements, ensuring consistency, compliance, and streamlined collaboration across teams.
4 · Collaborate
Instant Output & Review Enablement
Enabled Word/PDF exports for collaborative review.
Business analysts could annotate, update, and circulate documents instantly—supporting same-day stakeholder sign-off.
Technical Highlights
AI-based NLP framework for code-to-requirement mapping Automated SRS formatting using configurable templates Validation mapping intelligence for lookup vs. persistent data tables On-demand regeneration for new code commits Version-controlled output ensuring continuous traceability
// Pseudocode: Automated Event Data Validation Workflow
for module in js_files:
logic_blocks = parse_code(module)
for block in logic_blocks:
requirement = ai_model.extract_requirement(block)
srs.add(requirement)
srs.format(standard=”6-Part”)
srs.export(format=”PDF”)
Business Outcomes
The AI-driven documentation solution dramatically improved traceability, accuracy, and productivity across teams.
75%
Reduction in SRS preparation time
90%
Functional coverage across modules
100%
Traceability between code and documentation
Improved cross-team visibility between developers and analysts.
Accelerate Documentation with AI-Driven Precision
Empower your teams with intelligent automation that ensures accuracy, consistency, and compliance across your documentation lifecycle.