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We build and run the product while you sell it

For early-stage AI companies with a working idea, design partners waiting, and no time to build the thing properly. We take the product, the agents and the site, put them into production, and keep them running.

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Where we help

What we build for AI Startups.

Agent and copilot builds

The core product. We build agents and copilots that do real work against your data and tools, with guardrails on what they can touch, evaluation before release, and a rollback path when a model or prompt change makes things worse.

Production infrastructure

The part founders postpone. Auth, tenancy, logging, queues, rate limits, cost tracking per customer. Built once, properly, so your first ten accounts do not each need a manual workaround to stay alive.

Guardrails and monitoring

We instrument what the agent does, not just whether the service is up. Failed tool calls, refusals, latency, token spend, escalations to a human. You see behaviour drift before a design partner emails you about it.

Design partner onboarding

Early customers all want something slightly different. We build the configuration layer that absorbs it, so their requests become settings rather than forks of your codebase that nobody can merge back.

Website and launch pages

Webflow or Shopify, built to be edited by whoever is nearest, not by an engineer. Product pages, changelog, docs shell, waitlist and demo booking. Live in weeks, not queued behind the product roadmap.

Data and CMS layer

Headless CMS with Strapi or Webflow CMS so content, prompts, model settings and customer-facing copy live somewhere your team can change without a deploy and without asking us.

How it runs

Plan. Build. Run.

01

Thirty minute call

You show us what exists, who is waiting for it, and what you promised them. We say what we would build, what we would leave out for now, and whether we are the right people for it. No deck.

02

Fixed scope and price

We come back with a written scope, a timeline and a price. The first release is deliberately narrow, aimed at the thing your design partners actually asked for, so you have something demonstrable early.

03

Build in the open

You get working software in stages, not a reveal at the end. Agents go out behind guardrails and evaluation, infrastructure comes with monitoring from day one, and you can redirect us between stages.

04

We run it

Launch is the start of the retainer, not the end of the engagement. Monitoring, fixes against response targets, security updates, improvements each month, and a report you can put in front of your board.

Why infoloop

We do not hand over and leave.

  • We stay after launchMost agencies hand over a repository and disappear. We run what we build: monitoring, fixes to a response target, security updates and a monthly report. Handover happens when you have hired a team, not before.
  • Agents in production, not demosWe have put AI agents and copilots into production with guardrails, monitoring and rollback. That includes a support copilot for a fintech, where a wrong answer had consequences and needed containing.
  • You keep sellingThe founding team's time goes to customers, fundraising and design partners. Ours goes to the build queue, the deploy and the three in the morning alert. That division is the whole point of the arrangement.
  • Fixed scope before we startYou get a written scope, timeline and price before any work begins. That matters when the money is a seed round and every unplanned month of build is a month of runway you do not get back.
  • One team, product and siteThe agent, the app, the marketing site and the CMS are built by the same people. No handoff gap between the engineers and the web agency when the launch page has to be live before the product is.

What you get

Every engagement includes these, in writing, before work starts.

  • A production agent or copilot with guardrails, evaluation and a tested rollback path
  • Monitoring on agent behaviour, latency, cost per customer and failure rates, not just uptime
  • Auth, multi-tenancy, logging and per-account configuration for onboarding design partners
  • A Webflow or Shopify marketing site your team can edit without touching a repository
  • Headless CMS in Strapi or Webflow CMS for content, copy and customer-facing settings
  • A monthly report covering incidents, fixes shipped, security updates and what we changed

Who this is for

Three situations where this is the right call.

A demo that convinced people

The prototype won the meetings. Now three companies want access and it cannot survive two users at once, has no auth, and breaks when a model returns something unexpected. You need the real version without stopping the sales conversations.

Founders who can build but should not

Technical founders who are entirely capable of building this, and whose time is worth more spent in front of customers and investors. You want an outside team to hold the build queue and the on-call phone.

Funded, hiring slowly, shipping now

You have raised and are hiring engineers, but good ones take months to land and longer to become useful. You need the product live before then, built so the team you eventually hire can take it over cleanly.

Proof

Software we built, and still run.

Fintech support copilot

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Shipping an AI agent to production

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Questions

What buyers ask us first.

How much does it cost to build and run an AI product with infoloop?
Every engagement starts with a thirty minute call, after which infoloop returns a fixed scope, timeline and price in writing before any work begins. There is no hourly billing and no open-ended discovery phase, because early-stage companies are spending investor money against a runway and cannot absorb an unbounded build. The build is quoted as one fixed number. Running the product afterwards is a separate monthly retainer covering monitoring, fixes with agreed response targets, improvements, security updates and a monthly report. Scope changes are re-quoted before they are built, never after.
What happens after launch?
Launch is the beginning of the managed retainer rather than the end of the engagement. infoloop monitors the product, fixes problems against agreed response targets, applies security updates, ships improvements each month and sends a written report of what changed and what was done about it. For AI products the monitoring covers agent behaviour specifically: failed tool calls, refusal rates, latency, token spend and escalations to a human, because those degrade quietly in ways an uptime check will never catch. The retainer ends when an in-house team is ready to take over.
Will we own the code, and can our own engineers take it over later?
Yes. The client owns the codebase, the infrastructure accounts and the data throughout, and infoloop builds on the assumption that an in-house team will eventually inherit it. That means conventional tooling, documented deployment, environment configuration held in one place, and no proprietary layer that only infoloop can maintain. When a startup hires its own engineers, the handover is a scheduled piece of work with documentation and walkthroughs rather than a negotiation. Many clients keep the retainer running through the hiring period and taper it as the internal team takes on more.
How do you keep an AI agent from doing something harmful in production?
By constraining what it can reach and watching what it does. infoloop builds agents with explicit guardrails on the tools and data each agent can access, evaluation runs before a prompt or model change is released, and a rollback path that has been tested rather than assumed. In production the agent's behaviour is monitored, so failure patterns, refusals and escalations surface as data rather than as customer complaints. Where an answer carries real consequences, as in the fintech support copilot infoloop built, the design routes uncertain cases to a human instead of letting the agent guess.
Can you work with a technical founder who wants to stay involved in the build?
That is the usual arrangement. Most founders infoloop works with can build the product themselves and have concluded their time is better spent with customers and investors. Work is delivered in stages rather than revealed at the end, so a founder can review each release, redirect the next stage and make the architectural calls that matter without sitting in the build queue. Some founders write code alongside the team; others review pull requests and leave the rest. Both work. What infoloop holds is the delivery schedule and the on-call responsibility.
How quickly can an early-stage AI product go live?
It depends on scope, which is why the first release is deliberately narrow: the smallest version that does the thing design partners actually asked for, in production, with monitoring on it. Getting something real in front of early customers early is worth more than a complete product months later. A marketing site or launch page can typically go live well before the product itself, because it is not blocked by the same work. The written scope produced after the first call gives a specific timeline for the specific build rather than a general estimate, committed to alongside the price.

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Tell us what your design partners are waiting for

Thirty minutes on a call. Show us what exists and who is waiting for it. You get a fixed scope, timeline and price in writing, and an honest answer about whether we are the right team to build it.

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