As enterprises rethink how software is built, one thing is becoming clear: when applied correctly, AI doesn't just assist - it transforms. From accelerating delivery to improving quality and reducing deployment risks, AI is redefining software development.
At Akvelon, we’ve seen this transformation firsthand across client projects. By integrating AI into critical workflows, teams delivered up to 60% faster, improved build success by 8-12 percentage points, and reduced deployment setup times from days to seconds.
To scale these benefits, we launched an AI Productivity Challenge, capturing insights from 84 applied projects across real production environments. The results are already making an impact - here are some of the most compelling examples.
AI Use Case 1: Agentic AI Repository Documentation Workflow for Large-Scale Systems
Challenge
Enterprises with long product histories often manage monorepos so large that mapping ownership and dependencies can take months.
In one enterprise-scale project, Akvelon engineers worked with a 25 GB+ monorepo containing thousands of workflow configuration files owned by multiple teams.
Manual review would have taken months, slowing onboarding, refactoring, and audits.
Our Approach
To solve this problem, the team built an AI-driven agentic workflow integrated with Azure DevOps MCP.
The system scans repositories, analyzes directory structures, and retrieves ownership and dependency data directly from DevOps tools.
It then produces structured JSON summaries, giving teams consistent, predictable, and searchable documentation.
Measured Impact
Governance & Safety
- Secure internal repositories — no external data exposure
- Human verification for sensitive modules
- Guardrails in place for agentic actions
Where It Fits
Ideal for cloud providers, enterprise SaaS platforms, and regulated industries managing large, interconnected codebases that require fast, auditable documentation.
AI Use Case 2: Accelerating Feature Delivery With AI Instruction Packs
Challenge
Adding new features in large, multi-team environments often means repeating the same setup work — creating files, wiring connections, and checking that naming, logging, and build standards are met.
Even with Copilot, developers still spent days scaffolding the “first slice” of a feature before it could run successfully in CI/CD.
Our Approach
Akvelon engineers built AI-guided instruction packs — reusable templates that tell Copilot what to generate and how to connect it to the pipeline.
Each pack defines structure, naming, and build checks, so AI produces working code that compiles, logs correctly, and passes review on the first try.
As a result, teams can spin up a validated, runnable feature in hours rather than days.
Measured Impact
Governance & Safety
- Templates enforce formatting, naming, and CI validation
- AI suggestions reviewed via standard PR process
- Instruction packs execute within secure client environments only
Where It Fits
This solution is best suited for enterprises with large engineering teams that require consistent, high-velocity delivery. Such as cloud platforms, FinTech, and SaaS products that manage multiple feature streams at once.
AI Use Case 3: Automating API Configuration
Challenge
Creating and maintaining large API configurations for Kong and other services required engineers to manually follow and adjust OpenAPI specs. This process could take up to two hours per endpoint.
Each configuration needed careful mapping and verification across multiple YAML files, slowing down delivery.
Our Approach
Akvelon engineers combined Claude and GitHub Copilot to automate API config generation.
The system reads OpenAPI and YAML files, detects corresponding fields, and creates ready-to-use configurations with minimal input from developers.
Engineers now simply review and adjust the AI-generated results instead of writing them manually.
Where It Fits
Best suited for API development and integration projects where teams must generate and maintain large configuration files quickly and accurately.
AI Use Case 4: Measuring AI’s Impact on Software Delivery
Challenge
Many teams use AI tools in development, but few can quantify how much they actually help.
Without reliable data, it’s difficult to see whether AI truly accelerates delivery or simply adds extra noise.
Our Approach
As part of Akvelon’s AI Productivity Challenge, our engineers built a Quantitative AI Impact Tracker — a data pipeline based on Apache Beam that analyzes GitHub and CI/CD activity.
By comparing AI-assisted and non-AI pull requests, it measures merge speed, review effort, and build success, giving engineering leaders a clear, data-backed view of AI’s real productivity gains.
Measured Impact
Note: Real productivity gains backed by repository and CI/CD data (currently in advanced research stage).
Governance & Safety
- Uses only commit metadata — no code content exposed
- Makes dashboards available through a secure internal analytics layer
- Enables traceable, auditable reporting for enterprise environments
Where It Fits
Ideal for FinTech, HealthTech, and enterprise SaaS organizations seeking measurable, repeatable ways to track the ROI of AI in software development.
AI Use Case 5: AI-Friendly Coding Guidelines
Challenge
AI assistants deliver the best results when they understand a project’s structure and conventions. Inconsistent naming or unclear layouts often lead to inaccurate AI-generated code and additional rework.
Our Approach
Akvelon engineers established AI-friendly coding conventions, standardized file layouts and naming patterns, and introduced lightweight prompt templates. This structure helps Copilot and similar AI tools generate testable, production-ready code more consistently.
Measured Impact
(Currently in the research and scaling stage)
Where It Fits
Best suited for teams modernizing legacy systems or scaling large front-end and back-end projects where AI tools require clear structural context to deliver reliable output.
Who Benefits Most, and What Comes Next
Across all five cases, one pattern is clear: AI creates the most value when it’s part of real engineering workflows, not experiments.
The solutions developed during this challenge are already in production, helping clients across key industries achieve faster delivery, higher quality, and measurable productivity gains.
- FinTech: Faster, compliant releases with automated CI/CD and clear traceability.
- HealthTech: Audit-ready documentation and standardized reporting for regulated systems.
- Logistics & SaaS: Streamlined onboarding, accelerated deployments, and measurable delivery velocity.
Akvelon continues to apply, refine, and scale these AI-driven patterns, helping enterprises adopt AI safely, transparently, and with results that stand up to real-world performance.
How We Roll Out AI Safely and Transparently
Every AI solution Akvelon builds follows strict engineering and governance standards. All outputs are validated through human-in-the-loop reviews, with AI operating entirely within secure, client-controlled environments, ensuring no external data exposure. Each workflow includes built-in CI/CD quality gates to verify AI-generated outputs and transparent dashboards to measure real results, not just activity.
Ready to Turn AI into Business Impact?
Akvelon helps enterprises harness AI to drive faster releases, higher build success rates, and scalable software quality — all while maintaining full transparency and security.
Explore more: Applied AI and DevOps Solutions.
Or start a conversation with our team to see how structured AI adoption can bring measurable results to your business.
