At Quilltez, we’ve always aimed to stay ahead of the curve by embracing modern technologies. Artificial Intelligence (AI) is no exception. Over the past year, we’ve deeply integrated AI into our software development lifecycle — and the results have been remarkable.
We’re not just using AI for the sake of trend — we’re solving real problems and accelerating delivery. With AI embedded into our processes, we’ve increased our productivity by nearly 3x. That means faster turnaround times, better quality software, and reduced development costs.
Here’s a breakdown of how we use AI, where it fits in our workflow, and the impact it’s created.
1. AI for Code Generation & Automation
What We Do:
We use AI-powered tools (like GitHub Copilot, ChatGPT, and custom-trained models) to write boilerplate code, generate APIs, automate test scripts, and even suggest bug fixes in real-time.
Why It Matters:
- Speeds up repetitive tasks
- Reduces human error
- Developers can focus on core logic and architecture
Real Impact:
We saw a 40–50% reduction in time spent on routine coding tasks. What used to take 3–4 hours now takes less than one.
2. AI for Code Review & Quality Assurance
What We Do:
Our AI systems automatically scan pull requests for security issues, bad practices, and inefficiencies before human review. We also use ML models trained to detect issues specific to Laravel, Node.js, and React projects — our core stack.
Why It Matters:
- Reduces back-and-forth in the QA process
- Catches critical bugs early
- Makes code more consistent across teams
Real Impact:
We’ve achieved a 70% drop in post-deployment bugs and significantly faster sprint cycles.
3. AI for Requirement Analysis & Prototyping
What We Do:
We use natural language processing (NLP) models to analyze client requirements, generate user stories, and even create UI mockups based on functional descriptions.
Why It Matters:
- Converts vague business ideas into technical plans quickly
- Saves time in the discovery and planning phase
- Enhances clarity between clients and dev teams
Real Impact:
We cut our planning phase by 50%, allowing us to kick off development within days — not weeks.
4. AI in Testing & CI/CD Automation
What We Do:
AI generates and runs smart test cases based on code changes. It also prioritizes tests that are most likely to fail, and monitors production logs to flag anomalies in real-time.
Why It Matters:
- Enables shift-left testing
- Reduces manual QA load
- Improves reliability and uptime
Real Impact:
Our test coverage is now 95% automated, and deployment times have decreased by over 60%.
5. AI for DevOps & Infrastructure Management
What We Do:
Our AI tools help in optimizing cloud cost, predicting traffic spikes, auto-scaling infrastructure, and detecting early signs of system failure.
Why It Matters:
- Improves system resilience
- Saves on cloud spending
- Reduces need for manual infrastructure monitoring
Real Impact:
We’ve seen a 30% reduction in cloud costs and quicker recovery during incidents using predictive AI alerts.
6. AI-Powered Customer Support & Issue Tracking
What We Do:
We deploy AI chatbots and ticket classifiers that instantly respond to customer queries, log bugs, and assign them to the correct developer or team.
Why It Matters:
- Improves customer satisfaction
- Cuts response time
- Keeps the dev team focused
Real Impact:
Client support response time is now under 2 minutes on average — previously it was 10+.
Why It Works for Us
We don’t use AI just to automate — we use it to amplify. By embedding AI into every layer of our software development pipeline — from planning to production — we’ve unlocked a level of agility, precision, and output that simply wasn’t possible before.
Yes, it took some time to train the team, fine-tune the tools, and build internal workflows. But today, the payoff is clear:
- 3x faster delivery
- Fewer bugs
- Lower operational cost
- Higher team satisfaction
At Quilltez, AI isn’t just a tool — it’s a co-developer.