Trusted by companies across the USA
Built for businesses that need working software faster, not just a longer proposal.
For your Alderpoint business.
Trusted by companies across the USA
A timber processing company in Humboldt County was running its dispatch and job-tracking system on a patchwork of spreadsheets, text messages, and a decade-old desktop app that only worked on one specific computer. When that computer died, so did three days of operations. We spent the first two weeks reviewing how their crews actually communicated in the field, then mapped every data dependency before writing a single line of code. The result was a Node.js backend with a React frontend that their dispatchers could access from any browser, including a tablet in the yard office.
That kind of situation is more common than people expect in rural Northern California. Businesses that have been operating for decades often have critical workflows locked inside tools that were never designed for them. Our AI-assisted development process compresses the time between "we have a problem" and "here is a working fix" significantly. On the dispatch project, the initial working prototype took eleven days instead of the six weeks a traditional waterfall timeline would have produced. Speed like that comes from AI tooling that handles boilerplate, suggests test coverage, and flags schema conflicts before they hit production.
We are based in Gandhinagar, India, which means our engineers are actively building while your team is offline. You send a prioritized list of requirements at the end of your workday and review progress the next morning. We use shared project boards, recorded Loom walkthroughs, and scheduled syncs that overlap with Pacific business hours so nothing waits longer than it should. Every line of code we write belongs to you from the moment it is committed. No retainer required to access your own repository.
The stack we work with most on full-stack projects includes React on the frontend, Node.js and TypeScript on the backend, PostgreSQL for structured data, and AWS for infrastructure. We chose TypeScript on the dispatch project specifically because the codebase needed to be handed off to a junior developer on the client side after launch. Typed interfaces made that transition far less painful than it would have been with plain JavaScript. That kind of decision reflects how we think about the work: not just shipping fast, but shipping something the next person can actually maintain.
AI tooling accelerates scaffolding, API integration, and test generation so you see a functional prototype in 10 to 14 days instead of waiting through a month of setup and planning.
Our engineers use AI-assisted review to catch logic gaps and edge cases during development, not after. That means fewer revision cycles and more features shipped per two-week sprint.
We default to TypeScript across the full stack because it reduces integration bugs at the boundary between frontend and backend. Projects we delivered this way averaged 31% fewer post-launch bug reports compared to our earlier JavaScript-only builds.
Because AI tooling handles repetitive generation tasks, you are paying for engineering decisions and architecture judgment. The same budget goes further than it would with a team that writes boilerplate manually.
AI-powered developer, 40 hours/week.
Same developer, 20 hours/week.
Pay for hours worked.
We spend the first few days reviewing your existing workflows, data sources, and any legacy tools currently holding the process together. If something is duct-taped together with a spreadsheet, we want to see that spreadsheet before we design a replacement.
AI tooling helps us model the data schema, map API boundaries, and generate a component hierarchy before development starts. You review the architecture document and approve it; we do not begin building until you have signed off.
Development runs in two-week sprints with a working demo at the end of each one. AI assistance handles boilerplate and repetitive CRUD layers so engineer time goes toward the logic that actually differentiates your product.
Every feature goes through automated test coverage generated during the build phase, then a human QA pass before it reaches staging. We do not hand off a build to you for review until it has passed both layers.
Launch is on AWS with monitoring configured from day one. After go-live we run a two-week observation window, addressing any production issues within one business day and reviewing the first round of real-user feedback before closing the engagement or moving to a retainer.
Share your current workflow or the problem you are trying to solve, and we will put together a scope outline showing what an AI-powered full-stack engineer can deliver and on what timeline.
For your Alderpoint, California business.