AI-Accelerated SDLC: From AI Hackathon to 4x Velocity
Discover how our tailored hackathon helped a global AP automation leader compress a 4-month roadmap into 30 Day
Executive summary
A global leader in Procure-to-Pay (P2P) automation partnered with Zartis to directly address a strategic bottleneck: their product development cycle was too slow to keep pace with a competitive market demanding AI-powered features.
The problem
The challenge was not just about speed: It was about how work was organised. Sequential handoffs between Product, Engineering, and QA meant ideas took 8–15 weeks to reach working prototype. Teams had experimented with individual AI tools, but without a coherent workflow, the gains were fragmented.
The solution
We designed and executed a three-stage AI transformation: an intensive 2-day AI hackathon to prove the model, followed by integration of AI tooling into the live SDLC, and finally embedded expertise to refactor critical codebases to be AI-native. Using Claude Code orchestrated by our proprietary Z-CORA plugin, cross-functional teams worked simultaneously rather than sequentially.
The outcomes
- 4-month roadmap compressed to 30 days (75% reduction)
- 4x increase in development velocity
- 30+ engineers enabled on Z-CORA across multiple product teams
- AI shifted from “external assistant” to embedded co-creator across Product, Engineering, and QA
- Replicable AI-accelerated SDLC playbook delivered for independent use
About the client
The client is an enterprise software provider delivering cloud-based Procure-to-Pay automation solutions that handle eProcurement, invoice processing, vendor management, and payments for hundreds of enterprise customers, processing millions of invoices annually.
As a market leader competing on innovation velocity, the company recognised that traditional product development timelines, measured in months, were becoming a strategic liability. Competitors with faster iteration cycles were winning deals based on their ability to ship AI-powered features. The business needed to prove that AI could fundamentally accelerate how they built software, not just what they built.
The problem
The traditional SDLC required 8–15 weeks to move from initial concept to working prototype. Product teams spent 2–4 weeks creating detailed PRDs, engineering then broke these into user stories over several more weeks, and prototyping added another 1–2 weeks. By the time ideas reached validation, market requirements had often shifted.
This structure had a compounding cost:
Innovation paralysis
Product teams became risk-averse. Proposing an ambitious idea meant committing months of engineering time before knowing if it was worth building.
Expansive failures
Teams discovered fundamental flaws in features only after significant investment—too late to course-correct cheaply.
Comeptitive exposure
Losing deals to vendors who could demonstrate AI features while this client was still “working on them.”
Roadmap drift
A 4-month roadmap was under pressure, with no clear path to compression without increasing headcount.
Why they chose Zartis
The client needed more than access to AI tools—they needed a partner who understood both AI technical capabilities and SDLC transformation. Teams had already experimented with ChatGPT and GitHub Copilot individually, but without an orchestrated workflow, the gains were fragmented: Product’s AI-generated PRD used different terminology than Engineering’s AI-generated code, QA received requirements too late, and each team was improvising its own approach.
Zartis brought a proven framework for embedding AI across the entire development lifecycle. We delivered immediate, measurable results: a working proof of concept in two days, live SDLC integration, and embedded engineers refactoring production code alongside the client team.
how we worked together
The Zartis Approach
Rather than a one-off workshop or a theoretical playbook, Zartis designed a structured three-stage engagement that moved the client from experimentation to production-grade AI-native development.
Enablement Hackathon (Deecember 2024)
A focused 2-day hackathon to prove AI-accelerated development using a real backlog item. Cross-functional teams (Product, Engineering, QA) collaborated simultaneously using Claude Code orchestrated by Z-CORA.
AI-Augmented SDLC (December 2024)
Integration of Z-CORA into the live SDLC. AI-driven code review, automated testing, and intelligent code completion deployed across teams to enhance developer efficiency and code quality.
Embedded Expertise & AI-Native Refactoring (January 2025)
Zartis experts embedded alongside client engineers to refactor critical codebases, making them AI-native. Optimised for AI-driven analysis, prediction, and automation—compressing the 4-month roadmap to 30 days.
Our approach
What Made the AI Hackathon Work
The 2-day hackathon was structured around a real backlog item—not a toy example.
Using Claude Code orchestrated by Zartis’s proprietary Z-CORA plugin, four parallel workstreams ran simultaneously:
Product Thinking: Initial concepts shaped into a comprehensive PRD, with idea validation in under an hour
Refinement: PRD decomposed into INVEST-ready user stories and acceptance criteria
Quality: Acceptance criteria automatically converted into Gherkin test scenarios for immediate test scaffolding
Engineering: Front-end, back-end, infrastructure, and test automation code co-developed with AI-in-the-loop
Z-CORA maintained the shared semantic context across all four workstreams. Domain terminology, business rules, and acceptance criteria remained synchronised—eliminating the “telephone game” where requirements get distorted across handoffs.
Our approach
Key Technical Decisions
Orchestrated workflow over isolated AI tools
Individual use of ChatGPT or Copilot created inconsistency between teams. Z-CORA ensured that the PRD, user stories, acceptance criteria, and code all referenced the same domain concepts—zero reconciliation time between handoffs.
Parallel collaboration replacing sequential handoffs
Product, Engineering, and QA worked simultaneously rather than waiting for artifacts to be “thrown over the wall.” This eliminated approximately 80% of the calendar time embedded in the traditional handoff model.
AI as team member, not external tool
The workflow was designed so AI was continuously involved—generating options, flagging inconsistencies, maintaining context—rather than consulted occasionally. Teams stopped treating AI outputs as final answers and started iterating on them as collaborative drafts.
Real backlog item, validated against real baselines
All acceleration claims were measured against the client’s own documented sprint timelines from previous quarters, not hypothetical industry benchmarks.
the results
Outcomes & Measurable Impact
Roadmap Compression: The Core Result
The three-stage engagement delivered its central objective: a 4-month product roadmap compressed to 30 days. By refactoring the database, backend, and frontend simultaneously using AI-native workflows, the team achieved in one month what traditionally required four, without increasing headcount.
Metric | Before Zartis | After Zartis | Improvement |
Roadmap Completion | 4 months | 30 days | 75% faster |
Development Velocity | 1x | 4x | 4x increase |
Engineers on Z-CORA | 0 | 30+ | Multiple teams empowered |
Hackathon Deliverables: What Was Built in 2 Days
The AI hackathon produced production-ready artifacts from a real backlog item – not demos.
These were outputs the team could continue building from immediately:
With detailed acceptance criteria in ~2 hours (baseline: 3–6 weeks)
Interactive Prototype
Demonstrating core user workflows in ~1 hour (baseline: 1–2 weeks)
Comprehensive PRD
Including business context, user personas, functional requirements, and success metrics completed in ~1 hour (baseline: 2–4 weeks)
Epic Definition
With scope, dependencies, and acceptance criteria in ~45 minutes (baseline: 1–2 weeks)
Test Generation
Gherkin test scenarios auto-generated from acceptance criteria, with test automation scaffolding ready
Backend Consolidation
Scaffolded the backend architecture with API endpoints, data models, and business logic
Cultural Transformation
Redefining the Team's Relationship with AI
Perhaps the most significant and durable outcome was a shift in how teams relate to AI. The engagement successfully transitioned the team’s mental model from AI as an “external assistant” (something consulted when stuck) to AI as a “catalytic team member” (something actively co-creating throughout the workflow).
Evidence of this shift was observable during the AI hackathon itself: teams stopped copy-pasting AI outputs and started iterating on them. Product managers began asking “What would Claude suggest?” during prioritisation. Engineers used AI for architectural exploration, not just code generation. Cross-functional collaboration—rarely seen at this level in traditional sprints—became
the default mode of working.
The client’s teams progressed from Level 1 (“Human-driven with AI assistance”) to Level 2/3 (“AI-Assisted development with human oversight”) on the AI Maturity Matrix—a measurable step-change in organisational AI readiness.
The results
What This Enabled Next
The three-stage engagement delivered more than a compressed roadmap. It created the infrastructure and muscle memory for ongoing AI-native development:
Replicable SDLC Playbook
Documented workflows, prompt templates, and Z-CORA configurations the team can apply to future features independently—not a one-time demonstration.
Trained Teams
30+ engineers across Product, Engineering, and QA now have hands-on experience with AI-accelerated workflows and understand how to orchestrate tools effectively.
Faster Experimentation
With idea validation compressed, product teams can explore significantly more concepts in the same calendar time—discovering features that would never have been greenlit under the old model.
Competitive Response Capability
When competitors announce new AI capabilities, the client can prototype, validate, and ship competitive responses in weeks rather than months.