AI SDLC Implementation Services
Move your engineering team from ad hoc vibe coding to a governed AI SDLC that ships features faster and cheaper.
Transition to AI SDLC with MobiDev
AI SDLC turns scattered vibe coding into an AI-native software development lifecycle (SDLC). Transition to AI SDLC with MobiDev takes 2-4 months. Within this period, senior engineers audit how your team uses AI today, agree on approved tools and data rules, add build, test, and review gates for every AI change, and move those checks into CI/CD. Most teams run the daily AI workflow by month 2, with no pause in current releases.
What MobiDev Clients Saw after AI SDLC Implementation
Up to 45%
increase in software quality
Up to 30%
faster time to market
Up to 3h
saved per engineer weekly
Let’s Discuss How We Can Help You Succeed
Tell us about your software product needs
Your Team is Already Vibe-Coding, but Is It Enough?
No Shared Process
Each engineer uses AI their own way, so output quality depends on who picked up the ticket.
Bypassed Architecture
Vibe-coded features skip existing patterns and come back later as rework and tech debt.
Inherent Security Risks
AI-generated code is deployed with minimal review of its impact on business logic or security.
Benefits of Transitioning to AI SDLC with MobiDev
High Delivery Efficiency
An established process provides predictable timelines and consistent code quality.
Cutting Time & Budget
Code generation with AI helps you deliver features faster & at a lower cost.
Close Talent Gap
AI can write code in any language; senior developers supervise its output.
Business Risk Mitigation
Senior developers control how AI-generated features fit your business logic.
Security & Compliance
The mandatory AI SDLC revision stage ensures the code is secure & compliant.
Why Choose MobiDev as Your AI SDLC Implementation Company
Since 2018
Providing AI consulting & development services
7
AI SDLC Transition Projects in 2026
91%
Middle and senior engineers on the team
13%
Of all clients came through referrals from existing clients
5 years
Average Client Retention
9.1 NPS
As of the 2025 Client Survey
4.9
Client rating on Clutch
77%
Of clients are from North America and Europe
Transform Your SDLC into AI-native Software Development Lifecycle
How MobiDev Transforms SDLC into AI SDLC
AI Usage Audit
We audit how your team uses AI today, from scattered Copilot prompts to no AI at all, and find where code is written without anyone able to explain it.
AI Usage Policy
We agree with you on approved AI tools, accounts, a fallback tool, and which data never goes into AI, then add a basic project context file to the repository.
Daily AI Workflow
Every new feature starts with a defined scope and acceptance criteria, and every AI-generated change passes build, tests, lint, and senior code review before merge.
Team-Wide Adoption
All engineers work in the AI SDLC, AI configs are versioned alongside architecture decisions, and AI also handles tests, documentation, and code review support.
CI/CD Automation
AI code review and test generation run inside CI/CD, delivery metrics are tracked as team health trends, and controlled AI agent runs take on routine work.
What AI SDLC Looks Like after Transition with MobiDev
Requirements & Specifications
Your team decides what to build, and AI drafts user stories, edge cases, and API schemas in hours, with acceptance criteria set before any code is generated.
Design & Architecture
Senior architects make every trade-off decision and document module boundaries and contracts so AI agents build within them.
Implementation
AI writes code in small, bounded increments with tests in the same session, and engineers only submit code they can fully explain.
Verification
Every change passes build, tests, lint, and human review, plus a check that AI didn’t weaken tests or edit outside its scope.
Review & Deploy
An accountable engineer approves every merge, with PR descriptions stating what AI generated and where reviewers should look hardest.
Maintenance & Evolution
AI makes legacy code readable and generates safety-net tests before changes, so systems once too risky to touch can be updated in reviewed batches.
Choose Your Cooperation Model for AI SDLC Transition
AI SDLC Consulting
MobiDev’s Senior Engineers conduct a full audit of AI SDLC readiness and create a comprehensive transition plan based on your business goals and development process.
Dedicated Team
A dedicated team of MobiDev engineers creates an AI Software Development Lifecycle and builds, modernizes, or scales your product.
Team Augmentation
MobiDev’s Solutions Architect guides and supervises your in-house development team in their transition to the AI SDLC approach.
Implement AI SDLC with a Reliable Tech Partner
AI SDLC Implementation FAQ
Realistically, 2-4 months. We begin with an AI usage audit, followed by shared rules and project context. Once that’s done, we ramp up into practices that touch the entire team. We get most development teams running a daily AI workflow by month. Achieving full CI/CD automation for the team reaches completion around month. There’s obviously a scale factor depending on team size, complexity of the product, and how well-versed your team is with AI agents.
Not at all. The policy creation and code audit take place in the background, so your team won’t feel any impact on your current velocity. Once we have a policy drafted, we’ll test the new workflow on selected tasks to make sure everyone’s comfortable before we push changes team-wide.
That depends. Stage 1 of the assessment tells you where you currently stand. If your team already uses approved tools and enforces strict data guidelines, we can absolutely begin the process wherever you are.
We discover what tools your engineers are actually using today. We’ll scan your repo for custom AI configs and analyze how AI-generated code is reviewed throughout the team. You’ll be given a detailed report explaining current risks, process gaps, and code that nobody on the team can actually explain.
Only vendors that are part of your approved tools from Stage 2 of AI SDLC transition. Furthermore, we lock down those accounts to make absolutely sure they cannot store your data or use it to train their models. We’ll explicitly enumerate what data you never want sent to AI under any circumstances.
We can weave those restrictions into the policy from day one. If a suggested tool would put you in breach of contract, we find an approved alternative. If that isn’t possible, we simply leave that task as manual in your playbooks.
Your company retains full ownership. Just treat it as you would any code written by hand. We perform rigorous provenance and license checks before any code is merged.
Every change is put through the wringer. Automated build? Check. Automated tests? Check. Code linted and analysed for security? Check. Human developer review? Check. We look specifically for things like the AI adding new libraries it didn’t specify in the ticket or weakening existing tests.
We take extra precautions. First, we force the AI to write characterization tests. They snapshot how your software behaves today. Then, if a proposed change modifies that behavior, the tests are designed to catch the issue before release.
Installing a license on everyone’s machine just gives them a tool. No team guidelines, no standardized workflow, and worst of all, output quality will suffer based on who picked up the ticket. AI-SDLC puts processes around that tool usage so everyone generates high-quality output.
The choice is yours. We can continue maintenance on the configs and help update your process as your architecture changes. We can also provide all of the playbooks and context we’ve built for your team to run entirely in-house.