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

  • 75% reduction in SRS preparation time
  • 90% coverage of functional requirements across modules
  • 100% traceability between code and documentation

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.

  • Distributed Logic: Core operations—New Member Creation, Record Loading, Updating, and Deletion—spread across multiple JS files.
  • Manual Documentation: SRS creation required hours of manual review, interpretation, and validation mapping.
  • Complex Data & Validation Rules: Field-level logic, conditional checks, and table linkages were difficult to trace accurately.
  • Inconsistent SRS Format: 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
// Python 
// 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

  • Standardized documentation
  • AI-assisted regeneration
  • Reduced review cycles
  • Improved cross-team visibility
Accelerate Documentation with AI-Driven Precision
Empower your teams with intelligent automation that ensures accuracy, consistency, and compliance across your documentation lifecycle.
Speak with an expert