Project Name
Ksolves Reduces Manual Overhead Across the Development Lifecycle Using AI
A travel technology SaaS platform serving the Canadian and North American market runs a complex, multi-service codebase with regular feature releases and supplier integrations. As the product expanded, the volume of development workflow administration, writing tickets, appending specs, managing PR handoffs, grew right alongside it, consuming engineering capacity the team couldn’t afford to lose to process overhead. None of it created working software, yet it ate a disproportionate share of every sprint. Ksolves was trusted as an AI ML consulting partner to integrate Claude directly into the SDLC, building a custom skills layer connected to Jira and GitHub via the Model Context Protocol. Ticket authoring time dropped by more than 80%, and pre-work clarification cycles disappeared entirely.
- Manual Jira Ticket Creation: Every feature, bug, and task required a PM or developer to manually write title, description, acceptance criteria, and definition of done by hand, with inconsistent quality and critical fields regularly left incomplete.
- Missing Technical Specifications on Tickets: Developers frequently received tickets with no architecture notes or API references, creating back-and-forth between PMs and developers that added days of delay before work could even begin.
- Inconsistent Pull Request Quality: GitHub PRs varied widely in description quality and traceability back to Jira tickets, so reviewers spent time reconstructing context that should have been in the PR description in the first place.
- No Automated QA Checklist Enforcement: QA handoffs relied on individual engineers remembering to include the right information before raising a PR, with no automated gate ensuring QA-relevant criteria were actually captured.
- Tool Context Switching: Engineers constantly switched between Jira, GitHub, and documentation tools to manage workflow state, breaking focus and introducing the risk of information loss between tools.
- AI Tooling Without SDLC Integration: The team had access to AI tools, but none were connected to Jira or GitHub, so AI-generated content had to be manually copied and pasted, negating most of the productivity benefit.
Ksolves was trusted as an AI ML consulting partner to build a custom SDLC automation layer using Claude as the intelligence engine, connected to Atlassian Jira and GitHub through the Model Context Protocol, with purpose-specific skills encoding the team's own conventions rather than a generic AI assistant bolted on top.
- Custom Claude Skills Layer: Purpose-specific skills now handle each SDLC task, ticket creation with acceptance criteria and definition of done, technical spec appending, PR creation with structured descriptions, and QA checklist enforcement, each encoding team conventions so output matches engineering standards without manual formatting.
- MCP Integration With Jira and GitHub: Claude connects to live Jira and GitHub APIs, reading backlog context, creating and updating tickets, raising pull requests, and appending specifications directly in the tools teams already use, with no copy-paste and no context switching.
- Autonomous Ticket Execution: Claude reads a high-level requirement and generates a fully structured Jira ticket with title, description, acceptance criteria, definition of done, and labels, creating it directly in Jira, shifting PMs and developers from authoring to reviewing.
- AI-Generated Technical Specifications: For tickets requiring implementation detail, Claude appends a technical specification section covering architecture notes, API references, and edge cases directly to the ticket, before a single line of code gets written.
- PR Quality Gates: Claude generates pull request descriptions linked to the source Jira ticket, including a QA checklist and testing notes, so PRs arrive at review with consistent, complete context every time.
Technology Stack
| Category | Technology |
|---|---|
| AI/ML | Claude (Anthropic) |
| Integration | MCP (Model Context Protocol) |
| Platform | Atlassian Jira |
| DevSecOps | GitHub |
| Architecture | Custom Claude Skills Layer |
- 80% Reduction in Ticket Authoring Time: Claude now generates a fully structured ticket in under 60 seconds from a high-level requirement, down from 15-25 minutes of manual authoring per ticket.
- Pre-Work Clarification Cycles Eliminated: Every development ticket now includes an AI-generated technical specification appended at creation, removing the 1-2 days of back-and-forth clarification that used to happen before work could start.
- 100% PR Description Consistency: Every AI-assisted PR now arrives with a structured description, Jira link, and QA checklist, reducing reviewer context gaps to zero for covered workflows.
- Cross-Tool Context Switching Eliminated: Claude now handles all cross-tool writes via MCP, so engineers stay in a single interface for the full ticket-to-PR workflow instead of switching tools 8-12 times per ticket lifecycle.
- AI Adoption Embedded in the Workflow: With AI integrated directly into the tools teams already use, adoption rose significantly, with engineers now using Claude-assisted flows for the majority of new tickets.
Development teams on this platform spent a significant portion of every sprint on administrative tasks, writing tickets, appending specs, and creating PRs, none of which directly produced working software. Ksolves was trusted as an AI ML consulting partner to embed Claude end-to-end in the SDLC via a custom skills layer connected to Jira and GitHub through MCP.
Ticket authoring time dropped by more than 80%, pre-work clarification cycles disappeared entirely, and PR description consistency reached 100% across AI-assisted workflows. AI adoption moved from a standalone tool experiment to a workflow-embedded capability; engineers interact with it through the tools they already use, making adoption the path of least resistance instead of an extra step.
The same MCP integration layer and custom skills foundation now positions the team for sprint planning assistance, automated test generation, and release note drafting using the same architecture.
Is Your Engineering Team Losing Hours Every Sprint to Administrative Overhead?
