How we actually use AI

AI runs through every step of how Little Tiger builds software. Research, design, code, testing, performance, security. Faster delivery, lower costs, fewer surprises.

Published At

2 APR 2026

Author

Arjuna Shankar

Where AI shows up in our process

Research and scoping

Before we write a line of code, AI helps us understand what we're working with. Competitive analysis, technical feasibility, architecture patterns. Work that used to take a strategist three days now takes three hours. The strategist still does the thinking. AI does the legwork.

Planning and estimation

AI helps us break projects into accurate estimates by analyzing scope against similar builds. We catch complexity early, before it turns into budget surprises. Clients get tighter timelines and fewer change orders.

Design and prototyping

AI generates layout variations, component options and visual directions faster than any designer working from scratch. Our designers use that output as a starting point, not a finished product. They choose what works, throw out what doesn't and refine until the result has a point of view. AI handles volume. Designers handle taste.

Writing code

Our engineers write code with AI pair programming throughout. Boilerplate, utility functions, test scaffolding, API integrations. AI handles the patterns so our engineers can focus on architecture and business logic. The code still gets reviewed by humans. It just arrives faster.

Testing and QA

AI generates test cases from specifications, catches edge cases humans miss and validates accessibility compliance automatically. We still do manual QA for things that need human eyes, but the baseline coverage is broader than any manual process alone.

Performance

AI monitors build sizes, image compression, Core Web Vitals and load times across every project. We catch performance regressions before they ship. Automated audits run on every deployment, flagging anything below our standards.

Security and code review

Every commit gets scanned for vulnerabilities automatically. AI catches dependency issues, injection risks and configuration mistakes that manual review misses. Security runs continuously, not as a checklist before launch.

Every decision has a name on it

This isn't vibe coding

There's a term going around called vibe coding. You prompt an AI, accept whatever it generates and hope for the best. We do the opposite.

Every AI output at Little Tiger goes through human review before it touches production. Code gets reviewed by a senior engineer. Copy gets reviewed by a strategist. Design gets reviewed via consensus. Sometimes, AI proposes and humans approve. Sometimes we humans just direct them with force. Every decision has a person attached to it.

AI-generated code runs through the same pull request process as human-written code. Automated tests validate the output. A human reads it, questions it and signs off. If something breaks, there's a person accountable, not a model.

This matters because AI is good at patterns and bad at judgment. It can write a function in seconds, but it can't tell you whether that function belongs in your architecture. It can generate test cases, but it can't decide which edge cases actually matter for your business. Those calls require experience and context that models don't have.

We use AI to move faster. Humans move in decisions the right direction.

What this means for our clients

01Lower costs

AI makes our team faster. When research takes hours instead of days and boilerplate writes itself, we spend fewer billable hours on work that doesn't require human judgment. Those savings show up in your invoice.

02Faster delivery

Projects that would take 12 weeks with a traditional process ship in 6 to 8 with ours. Not because we cut corners. Because AI handles the repetitive work while our engineers focus on the problems that matter.

03Stronger security

Automated scanning on every commit. OWASP Top 10 checks, dependency audits and configuration reviews running continuously. Not a phase at the end of a project. A constant.

04Everything under one roof

Most teams split the work across shops: design here, development there, AI somewhere else. Every handoff loses context and adds cost. We keep research, strategy, design, development, AI and operations in one team. One codebase. One conversation.

Want to see what AI-native actually looks like?

Tell us what you're building. We'll show you how we'd approach it.