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Product and MVP development

AI Product & MVP Development in Boston

The best AI Product & MVP Development in Boston, 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.

Boston's technology economy is built around research: biotech and life sciences in Kendall Square, robotics, and the universities that feed both. Companies here often hold sensitive research or patient data and have scientists, not software engineers, in charge of the early product.

Boston spinouts usually have the science and a grant or seed round, but no product team. The build turns a research prototype into software a partner or customer can actually use.

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.

Boston, Massachusetts

Who builds here

Biotech and life sciences in Kendall SquareMIT, Harvard and university spinoutsRoboticsHospitals and health care
Best fit in Boston

On-Premise LLM Deployment

Boston life sciences and health companies hold research and patient data that rarely can leave their own environment, which makes a private model the usual starting point for any AI work.

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 Boston

What actually applies here.

Regulation and data

Massachusetts has one of the oldest data security regulations in the country, 201 CMR 17.00, which requires any business holding Massachusetts residents' personal information to maintain a written information security program, including encryption of personal data sent over public networks and stored on laptops. Health data adds HIPAA, and clinical research adds FDA expectations around data integrity and validation for software used in regulated work. Hospitals and pharma partners will usually ask about all three before a pilot.

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

Four or more hours of daily overlap with Eastern time, calls in your morning, and same-day replies on working days.

Can you work with patient or research data?

Yes, with the safeguards your agreements require: data stays in your environment, access is named and limited, work happens on synthetic or de-identified data where possible, and the paperwork your compliance team needs is agreed before work starts.

Do you understand regulated research software?

We build with audit trails, versioned data and documented testing, which is what reviewers look for. Formal validation for a regulated submission is run with your quality 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.