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Product and MVP development

AI Product & MVP Development in Salt Lake City

The best AI Product & MVP Development in Salt Lake City, at the best available price.

$0 upfrontPay per accepted checkpointSenior engineer, no agency markup

A real, production-grade build you can put in front of users or investors. Frontend, backend, mobile, the agentic AI underneath, and the cloud or private infrastructure it runs on, as one engagement rather than four vendors.

Salt Lake City and the Lehi and Provo corridor, known as Silicon Slopes, have produced a run of large SaaS companies in customer experience, analytics and education software. The talent pool is strong in enterprise software and sales, with lower costs than the coasts.

Founders on the Silicon Slopes often come out of established SaaS companies and know their market well. The build gets the first version in front of customers quickly, built to scale past the first hundred accounts.

What you get

  • Full-stack build on Next.js, Node or Python, with a database and API design that survives past the MVP
  • AI where it earns its place: LLM features, retrieval over your own documents, agent flows that call real tools, voice
  • Auth, payments, analytics and an admin surface, not a demo that breaks on the second user
  • Deployment across AWS, GCP or Azure, or private and on-premise hosting where the data demands it
  • Clean, documented code on your own infrastructure, written to make us replaceable on purpose
Working MVPs from $1,000. Full production builds from $12,000.

Final pricing depends on scope, features and complexity, so we settle it on a call before any work starts.

Invoiced in USD, payable by Wise or bank transfer.

Request a callback

You speak to the engineer who does the work. No sales rep, no deck.

No spam and no sales team. You talk directly to Neeraj.

Salt Lake City, Utah

Who builds here

Enterprise SaaSSilicon Slopes in Lehi and ProvoUniversity of Utah and BYUFintech and payments
Best fit in Salt Lake City

Software Development Company

Utah's SaaS companies are past the idea stage and need dependable senior engineering capacity to ship their roadmap, which is what a dedicated engineer provides.

Three founders around a laptop in a sunlit office, celebrating the moment their product went live
Timeline of a production MVP built in six weeks and paid per checkpoint. Today costs $0. Week 1 locks scope and architecture; weeks 2 to 4 build the core product with a demo every week; week 5 adds sign in, billing, admin and analytics; week 6 hardens the product and hands it over, live on your own accounts. Each stage ends in a checkpoint that is accepted, then invoiced.
Four checkpoints, each invoiced only after you accept it. Nothing is due before the first one.
How you pay

Get it built at $0.

That is not a discount. It is when you pay. The work is split into checkpoints with acceptance criteria written down before anything starts, and each checkpoint is invoiced only after you have seen it and accepted it. No deposit.

$0 to start
You hold every dollar until a checkpoint is delivered and you accept it. No approval, no invoice.
Fixed cost, unlimited features
Or hire the team outright: one fixed monthly cost, unlimited feature development, any stack.
The engineer takes your call
The person on your first call is the one who architects and writes it. No account managers, no bench time.

A US agency quotes $50,000 to $150,000 for the same build and asks for 40 to 50% of it before a line is written. Account managers, project managers, sales commission and bench time. None of it appears in your product.

How it works

Three stages, nothing hidden.

01

Twenty-minute call

You talk to the engineer who will build it, not a sales rep. We scope the problem and you get told plainly if this is the wrong fit.

02

Fixed proposal in 48 hours

Fixed price, fixed timeline, defined deliverables, in writing. No hourly billing surprises and no open-ended scope.

03

Weekly demos until it ships

Working software every week, direct access over Slack or WhatsApp, and documentation handed over at the end.

Working in Salt Lake City

What actually applies here.

Regulation and data

Utah was the first state to pass an AI-specific consumer law. The Utah Artificial Intelligence Policy Act requires disclosure when people interact with generative AI in certain settings: regulated professions must disclose up front in high-risk interactions, and other businesses must disclose when a person clearly asks. It was narrowed in 2025, but those disclosure duties remain. The Utah Consumer Privacy Act applies to larger businesses, with a higher threshold than most states.

Contracting and payment

You contract with an individual consultant based in India rather than a US entity. Invoices are issued in USD and paid by Wise or bank transfer, with no payroll and no benefits load on your side. Whatever documentation your finance team or counsel needs from an overseas contractor is provided before work starts.

Working hours

Calls in your morning, Mountain time, a shared window at the start of your day, and work handed over overnight so the next round is waiting when you start.

Do we have to tell users they are talking to an AI in Utah?

In a regulated profession and a high-risk interaction, yes, up front. Otherwise you must disclose when a user clearly asks. Our agents disclose by default, which also builds trust.

Can you work alongside our existing engineering team?

Yes, and that is the usual setup: one senior engineer working in your repositories, to your standards and processes, with code review from your team.

Is three days realistic?

For a working, demoable version of a core idea, yes, and that is what the three-day build is. A full production system with billing, admin and hardening takes weeks, not days. We will tell you which one your situation actually needs.

Do we own the code?

Entirely. It ships into your repository on your own cloud accounts, documented so your team or your next hire can pick it up without us.

Can you also host the AI model privately?

Yes, and as part of the same engagement. If compliance means inference cannot leave your network, the serving layer and the application are built together rather than handed between vendors.