Trusted by companies across the USA
SaaS and tech-adjacent businesses in South Orange County get working builds faster, without bloated retainers.
For your Aliso Viejo business.
Trusted by companies across the USA
A med-tech software company in Aliso Viejo spent four months waiting on a contractor to deliver a patient intake portal. The contractor was competent. The pace was the problem. Every revision cycle added two weeks, QA ran behind, and the product launch slipped from Q2 into Q4. That kind of delay does not come from a skill gap; it comes from building the way developers built ten years ago.
We work differently. Our full-stack engineers use AI tooling throughout the actual build: scaffolding boilerplate in minutes instead of days, generating and running test suites before a human reviews a single line, and drafting TypeScript interfaces from API contracts before the backend is even finished. The productivity gap is real. A task that used to absorb a full day of focused coding now takes a morning. That surplus goes into the work that actually matters: architecture decisions, edge-case handling, and making sure the thing you ship is the thing you designed.
Aliso Viejo sits inside one of the denser tech-adjacent corridors in Southern California, with a high concentration of SaaS vendors, health IT firms, and B2B software companies. Those businesses often have the same problem: a working idea, a tight timeline, and a development backlog that keeps growing faster than it gets resolved. We have worked with companies across California in exactly that position since 2015. The model is simple. You get a senior engineer who uses AI tools to move at roughly twice the pace of a conventional developer, working from India with daily async updates and overlap during your Pacific business hours.
One thing worth being honest about: AI-assisted development is not magic. It does not replace judgment on database schema decisions, infrastructure choices, or the kind of product thinking that determines whether you are building the right thing at all. On a recent project, we used AI tooling to accelerate the React frontend build significantly, but the PostgreSQL schema design and the AWS infrastructure setup still required careful human reasoning. The tools compress the mechanical work. The engineering judgment is still ours.
You see a working, deployed build at the end of every two-week sprint. If priorities shift, the next sprint reflects it before too much is built in the wrong direction.
AI tooling handles test scaffolding, boilerplate generation, and first-pass linting automatically, so the engineer spends hours on architecture and logic instead of setup tasks. You get more reviewed, production-ready code per billing week than a conventional hire produces.
Shared TypeScript types between your React frontend and Node.js backend eliminate an entire category of runtime bugs. We enforce this from day one, not as a retrofit after something breaks in production.
All code, repositories, and AWS infrastructure resources are transferred to your accounts at the start of the engagement. No vendor lock-in, no hostage code. If you decide to bring development in-house later, you hand off a clean, documented codebase.
AI-powered developer, 40 hours/week.
Same developer, 20 hours/week.
Pay for hours worked.
We spend the first few days reviewing your existing system, your API contracts if they exist, and the workflow your team actually uses today. We ask specific questions over Zoom and document what we learn in a shared spec before any code is written.
We run the architecture and component plan through AI-assisted planning tools to identify gaps, flag dependency conflicts, and generate a realistic sprint timeline. You review the plan and approve it before development starts.
Development runs in two-week sprints. AI tooling accelerates boilerplate, test generation, and interface scaffolding so the engineer focuses on the logic-heavy work. You get a Loom walkthrough of every completed sprint.
Automated test suites run on every commit, but a human engineer reviews every pull request before it merges. Edge cases, security checks, and performance profiling happen here, not after launch.
We deploy to your AWS environment, hand off documentation and access credentials, and stay available for the first 30 days post-launch to catch anything that surfaces in real usage. Post-launch support is structured as a lightweight retainer, not an open-ended billing relationship.
Tell us what you are trying to ship and we will review your requirements and map out a sprint plan, so you know exactly what an AI-powered full-stack engineer can deliver for your project and when.
For your Aliso Viejo, California business.