AI startups
AI MVP development for startups, from demo to paying users
Infoloop is an AI MVP development company for early-stage AI startups. Your prototype performs in a demo, and design partners are waiting for access. Infoloop delivers the production version: authentication, isolated customer data, model guardrails and a launch page. Scope, budget and timeline are agreed in writing before work starts.
Talk to our teamBuild progress, six modules
Build progress
Auth
Shipped and running in production.
- Investor update
- Sent monthly
- Customer demos
- 3 booked this week
From a working demo to a production release
A prototype that earned early interest. Design partners waiting for access. Limited runway, and no spare engineering capacity for a production release.
Most founders have the idea and the early validation. What they lack is engineering capacity. The prototype performs in a demo and fails with two concurrent users. There is no authentication, and customer data is not isolated. One unexpected model response can take the entire application down.
Recruiting is the obvious answer, and it takes months. Senior engineers are difficult to hire and slow to onboard. Meanwhile, three prospective customers are requesting access and the launch page is still waiting. Every hour a founder spends coding is an hour away from customers and investors.
- An assistant or agent in production, with firm guardrails.
- Authentication, tenant isolation, job queues and audit logs.
- A launch page your team updates without an engineer.
Infoloop delivers the production version with monitoring active from launch. The smallest useful release ships first, then the rest of the roadmap. When your own engineering team is ready, a planned handover follows. Infoloop is headquartered in India and works with companies worldwide.
Measured results
Where early AI products stall
Four issues founders raise on a first call, and how each one is resolved.
A working demo that is not ready for customers
Three companies want access, and the prototype fails with two concurrent users. The production version adds authentication, isolated customer data, job queues so no request is lost, and an audit log of every action.
No visibility into AI behavior in production
Uptime monitoring alone reveals very little. Monitoring covers how often the AI fails a task, refuses or escalates to a person. It also tracks response time and model cost per customer. Your team sees quality drift before a design partner reports it.
Each early customer requests a different configuration
Design partners each request adjustments. Per-customer settings replace custom code. A request becomes a configuration option, not a forked codebase that nobody can merge back.
Founders are the development bottleneck
Founders can write the code, but their time is more valuable with customers and investors. Recruiting takes months. Infoloop takes over AI product development and writes maintainable code that your future engineering hires can adopt.
Where early AI products stall
4 problemsProblem 1 of 4
A working demo that is not ready for customers
What we do
Three companies want access, and the prototype fails with two concurrent users. The production version adds authentication, isolated customer data, job queues so no request is lost, and an audit log of every action.
- Sign-in
- Information kept apart
- A record of what happened
Built while you sell. Supported after launch.
Case studies
Measured results from AI systems in production.
An AI support assistant that cut manual ticket work by 72%
An AI support assistant cut first response time from hours to under two minutes and reduced manual ticket handling by 72%.
- manual ticket hours
- -72%
- first response time
- <2 min
- to first version live
- 5 weeks
Your project
Planning takes about a week. The first release date is agreed in writing, with monitoring active from day one.
What an AI startup engagement includes
For founders who need a production release without pausing sales.
A release date in writing
Planning takes about a week. The first release date is agreed in writing, with monitoring active from day one.
Billing that fits the stage
A fixed price when the scope is clear, hourly when it will change. The rate and an estimate are agreed first, so runway is not spent on surprises.
Guardrails before customers see it
Strict limits on data and tool access, evaluation before every release, and a fast rollback if quality drops.
Content your team controls
Copy, prompts and settings live in Webflow or an editor, so your team can update them the same day.
Support until you hire
Monitoring, fixes within an agreed service level, security updates and a monthly report for your board.
Companies that use software made by Infoloop
Developed on a standard stack your future engineers already know: Next.js, Node.js, React and Webflow.
- NHS
- HSBC UK
- Infosys
- GoCardless
- Makson
- Brompton
- HCFS
- projects delivered
- 50+
- countries
- 6
- uptime on software we support
- 99.9%
- average client rating
- 4.8
What our clients say
Priya M. · card_declined · resolved 2m ago
Card declined at checkout
Agent draft
Your bank declined the payment for insufficient funds. Nothing was charged. Try again with another card and the order will hold your price for 24 hours.
Confidence 96%
They shipped our support agent in five weeks and it has run ever since. Response time dropped from hours to two minutes.
COO, fintech scale-up
Read the case studyBlogs and insights
FAQs
Questions from AI startup founders
Direct answers. If your question is not listed, raise it on the first call.
What does an AI MVP cost?
A first release with a clear scope has a fixed price, with a delivery date and a list of inclusions and exclusions. Discovery and changes after launch are billed hourly, also called time and materials. A dedicated developer or team is billed hourly or monthly. In every case, the rate and an estimate are agreed in writing before work starts.
How fast can an AI MVP go live?
Most projects go live in 4 to 8 weeks, and larger systems go live in agreed phases. The first release is the smallest version that meets your design partners' core requirements, with monitoring from day one. A marketing launch page can go live sooner. The delivery date is agreed in writing, next to the estimate, before work starts.
Who owns the code, the prompts and the data?
You do, from day one. Code sits in your repository, prompts and settings in an editor your team controls, and data in your own cloud accounts. The stack is standard, such as Next.js and Node.js, so future engineering hires take over through a planned handover, not a negotiation.
How do you keep an AI assistant within limits in front of paying users?
By restricting what it can access and measuring what it does. Each assistant receives only the tools and data its task requires. Every change is evaluated on real examples before release, with a rollback path. Where an incorrect answer carries risk, the assistant escalates the question to a person.
Can I stay involved during development?
As much as you like. Work ships in stages, so you review each release and adjust the next one. You can contribute code alongside the team or simply review the weekly changelog. Infoloop remains accountable for the delivery date either way.
What if we change direction after the first release?
You can pivot between stages without losing completed work. Copy, prompts and settings live in an editor your team controls, so a messaging change needs no engineer. A larger change is priced in writing before it starts, so you decide whether it justifies the cost and the time.
Tell us what your design partners need
A 30-minute call about your prototype and the customers waiting for it. You leave with a direct recommendation on whether to proceed. A written scope, estimate and timeline follow.