AI Adoption for Tech Teams

"Vibe coding" means letting AI generate code without proper oversight, review, or understanding. It often leads to security vulnerabilities, poor architecture, and unmaintainable code. Proper AI adoption means using AI as a productivity multiplier while maintaining human oversight, code quality standards, and architectural integrity. It's about structured workflows, not just prompting AI to write code.

We recommend a mix of tools depending on your workflow: Claude Code for agentic coding and complex refactoring, Cursor for IDE-integrated AI assistance, parallel AI coding sessions for tackling multiple features simultaneously, AI-powered code reviews for catching issues, and various AI tools for documentation, testing, and monitoring. We'll recommend the right stack for your team's needs.

AI can assist at every stage: Ideation & Architecture - brainstorming, technical design, and system architecture; Data Modelling - schema design and database optimisation; Development - coding, refactoring, and pair programming; Testing - test generation and coverage analysis; Deployment - CI/CD optimisation and infrastructure as code; Monitoring - log analysis and incident response. We help you integrate AI across all these stages.

Teams typically see 3-5x improvement in development velocity for appropriate tasks. As a real example, we built and released MintyAI in just 2 weeks - a complex system with data processing, AI workflows, algorithms, and matchmaking that would have taken 4-5 months to build traditionally. The key is knowing when and how to use AI effectively.

We teach structured workflows that include: human review of all AI-generated code, architectural guidelines that AI must follow, security-focused prompting practices, AI-assisted code reviews (AI reviewing AI-generated code with human oversight), and clear ownership and accountability. The goal is to use AI as a tool that augments human expertise, not replaces critical thinking.

Security is a primary concern. AI can introduce vulnerabilities if not properly supervised. Our training covers: secure prompting practices, mandatory security reviews for AI-generated code, avoiding exposing sensitive data to AI tools, using AI for security analysis and vulnerability detection, and establishing clear policies for what code AI can and cannot touch (e.g., authentication, payments).

Teams typically start seeing productivity improvements within the first week of training. Full integration across the SDLC usually takes 2-4 weeks depending on team size and existing workflows. We provide ongoing support to ensure adoption sticks and evolves as AI tools improve.

We provide both. Our engagement includes hands-on training sessions where engineers learn by doing - writing prompts, reviewing AI output, and practicing workflows. We also provide documented guidelines, best practices, and playbooks that your team can reference. We can also embed with your team temporarily to help establish the new workflows.

Great! We'll assess your current usage, identify gaps and opportunities, and help you level up. Many teams use AI tools but don't use them effectively, safely, or to their full potential. We'll help you establish better practices, expand usage to new areas of the SDLC, and ensure proper oversight is in place.