Trusted by companies across the USA
Senior SaaS engineering at AI throughput, billed by the hour
A founder came to us with a half-built subscription product and a billing flow that double-charged customers during plan upgrades. We rebuilt the tenant isolation, the Stripe webhooks, and the admin dashboard in the time most teams spend writing the spec. You get senior SaaS engineering moving at the pace the best available AI coding tools now allow.
We match you within 48 hours.
Trusted by companies across the USA
A SaaS founder reached out after his contract team quoted nine weeks for a usage-based billing rewrite and a tenant-level reporting dashboard. His existing app on PostgreSQL had three customers sharing a row-level security policy that leaked one account's invoices into another's view. That kind of bug erodes trust fast, and he needed it fixed before a board demo. We scoped it on a Tuesday call and had a working build in his hands eleven days later.
This is the AI-powered model, and it works differently from hiring a regular contractor. Our engineers run the best available AI coding tools on every task, which means boilerplate, test scaffolding, migration scripts, and repetitive CRUD endpoints get drafted in minutes instead of hours. The engineer then reads every line, fixes what the tool got wrong, and makes the architecture decisions a machine cannot. You pay a higher hourly figure than a traditional developer, but the hours add up to far fewer, so the total you spend on a deliverable comes out lower.
Multi-tenancy is where this pace actually matters. A SaaS product lives or dies on whether tenant A can ever see tenant B's data, and getting that wrong is the most expensive mistake in the category. We use the AI tooling to generate the exhaustive test matrix that proves isolation holds across every query path, something a human writing tests by hand tends to cut short under deadline. The speed buys you more coverage, not less.
Here is the honest part. AI tooling does not speed up the thinking that makes a SaaS product good: deciding how to model a tenant, where to draw the billing boundaries, how to handle a customer mid-cycle plan change without a refund nightmare. Those calls still take a senior engineer sitting with your actual workflow over a series of calls. The tools make the typing fast; they do not make the design decisions, and we do not pretend otherwise.
Your product serves many customers from one codebase, and each tenant's data has to stay sealed off. We design the isolation in PostgreSQL up front and prove it with generated test suites, so a reporting query never crosses an account boundary.
The best available AI coding tools draft migrations, API endpoints, and test scaffolds while the engineer reviews and corrects. Repetitive work that used to eat half a day gets done before lunch, and the saved hours show up on your invoice.
The AI accelerates output, but a person who has shipped subscription products owns the result. They catch the webhook race condition the tool missed and decide when a generated solution is wrong for your billing rules.
Stripe is easy to wire up and easy to get subtly wrong on proration, failed payments, and mid-cycle upgrades. We handle the webhook idempotency and dunning logic so customers are not double-charged when they switch plans.
Our team works from Gandhinagar and keeps a daily overlap with US Eastern and Pacific business hours. You send feedback in your afternoon and review fresh progress the next morning, which turns the time difference into extra throughput.
You own every line, every repository, and every credential the moment work starts. We sign an NDA and a clear contract before the first commit, and there is no lock-in to us as a vendor.
An admin dashboard with role-based access and tenant filters that traditionally runs three weeks lands in eight to eleven days. The AI tooling drafts the repetitive React components and Node.js routes; the engineer spends saved time on the logic that actually needs a brain.
One engineer paired with the best available AI coding tools closes more tickets per day than a traditional developer working alone. Test scaffolds, type definitions, and CRUD endpoints get generated, reviewed, and corrected in a fraction of the usual time.
Because the tooling handles the tedious typing, the engineer has hours left for the test matrix that proves tenant isolation and for the edge cases on Stripe webhooks. More coverage gets written, not less, and a human reviews every generated line before it merges.
The hourly figure is higher than a regular contractor, but the deliverable takes far fewer hours. A billing rewrite that bills for nine weeks elsewhere wraps in under two, so the number at the bottom of your invoice comes out smaller.
AI-powered developer working 40 hours/week on your project.
Same AI-powered developer, 20 hours/week. Consistent AI-augmented progress.
Pay for hours worked. Code reviews, sprints, or consulting with AI-powered output.
Hire a complete AI-powered team. Developer + designer + QA + PM, all using the best AI tools. Maximum output, one monthly rate.
This is the day-to-day delivery workflow, not the hiring process.
Get StartedWe sit with your actual product over a series of calls and map how a tenant signs up, pays, and uses the app. Before any code, we pin down the billing rules and the isolation model, because those two decisions shape everything else in a SaaS build.
We use the AI tooling to sketch the schema, the API surface, and the migration path quickly, then the engineer revises every choice against your real constraints. You see a concrete architecture plan in days, not after a long quiet stretch.
The best available AI coding tools draft the repetitive React and Node.js work while the engineer reviews each line and writes the logic the tools cannot. You get a working build to click through in roughly a week, then iterate from something real instead of a wireframe.
We generate an exhaustive test matrix that proves one tenant can never reach another's data across every query path. A senior engineer then reviews the Stripe flows by hand, because a billing bug is not the kind of thing you trust a machine to clear alone.
We deploy to AWS with monitoring on the billing webhooks and the tenant boundaries, then watch the first real customers move through the app. After go-live we stay on to tune queries and ship the next round of features at the same pace.
Send us your current product or your spec and we will map the tenant model, the billing flow, and a realistic timeline for an AI-powered SaaS developer to deliver it. You will see where the speed advantage applies and where it does not, with no obligation to continue.
Describe your project and requirements.