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Software for marketplaces and consumer apps

For teams running a two-sided marketplace or a consumer app where growth has outpaced the platform. We build the matching, payments, moderation and retention work, then run it under a monthly retainer.

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Where we help

What we build for Consumer Platforms.

Marketplace mechanics

Search, ranking, availability and booking are the product on a marketplace. We build the flows both sides actually use, including the awkward ones: cancellations, no-shows, partial refunds and the states in between.

Payments, payouts and disputes

Split payments, held funds, payout schedules, refunds and chargebacks. We build the ledger and the admin screens finance and support need, so nobody is reconciling a marketplace in a spreadsheet.

Trust, safety and moderation

Listing checks, fraud signals, review abuse and reporting flows. We build queues with written rules, escalation to a human and an audit trail, so a moderation decision can still be explained six months later.

Retention and lifecycle messaging

Onboarding, activation, dormancy and win-back. We instrument the funnel first, then build the email, push and in-app prompts against real cohort data rather than a guess about what users want.

Content, SEO and landing surfaces

Category, city and seller pages are how most marketplaces get found. We build them on a headless CMS, Webflow CMS or Strapi, so marketing can publish without waiting for an engineering sprint.

Support copilots and self-serve

Support volume grows with users. We put a copilot in front of your agents, drafted from your own help content and order data, with guardrails, monitoring and a switch to turn it off if answers drift.

How it runs

Plan. Build. Run.

01

A 30 minute call

You describe the platform and where it hurts: liquidity, retention, load, backlog. We ask about your stack, your data and who currently keeps it running. No deck, and no invoice for discovery.

02

Fixed scope, timeline and price

We write down what we will build, in what order, by when and for how much. Anything genuinely unknowable up front gets split out as a short, separately priced piece of work rather than hidden inside a range.

03

Build in slices, behind flags

Work ships in small pieces you can see running. Every release goes out behind a flag with a rollback path, so a change to checkout or ranking can be turned off in minutes instead of hotfixed at midnight.

04

Then we run it

Launch starts the retainer. Monitoring, fixes with agreed response targets, security updates, a steady line of improvements, and a monthly report on uptime, incidents and what we suggest doing next.

Why infoloop

We do not hand over and leave.

  • We do not hand over and leaveMost agencies bill the build and disappear at launch. Our managed retainer is the normal end state: monitoring, fixes to response targets, security updates and a monthly report you can forward.
  • Guardrails before autonomyWe have put AI agents and copilots into production with limits on what they can do, monitoring on what they did, and a rollback path. On a consumer platform that matters more than model choice.
  • One team for web, store and platformMarketing pages, the store, the CMS behind them and the platform work sit with one team. Fewer handovers means fewer of the integration gaps that surface as a broken checkout on a Friday.
  • Numbers we will stand behind50+ products shipped, clients in 6 countries, 4.8 average rating, 99.9% uptime and $1.5M+ of client revenue driven. We would rather show you those than claim sector awards we do not have.
  • Honest about what we have not doneWe have no published marketplace case study. We have shipped commerce, ERP, attendance and copilot work, and we will tell you which parts of your build are familiar ground and which are not.

What you get

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

  • A staging environment seeded with data for both sides of the marketplace
  • An event tracking plan covering signup, first action, repeat use and churn
  • Feature flags and a documented rollback path for every release we ship
  • Monitoring and alerts on the flows that lose money when they break
  • A moderation queue with written rules, escalation and an audit trail
  • A monthly report on uptime, incidents, work done and what we suggest next

Who this is for

Three situations where this is the right call.

One side of the marketplace is thin

You have sellers and not enough buyers, or the reverse. The fix is rarely more marketing spend; it is usually matching, search, availability and how fast a first transaction can complete. We work on those before anything else.

Signups hold up, week four does not

Acquisition works and the 30-day chart does not. Nobody can say which step loses people, because the events were never instrumented properly. We fix the measurement first, then the onboarding and lifecycle messaging it points at.

The platform outgrew the people running it

One engineer holds the whole system, the backlog is all maintenance, and every campaign is a risk because nobody is sure what breaks under load. We take on the running so your team can build product again.

Questions

What buyers ask us first.

Have you built a consumer marketplace before?
Not one we can point to as a published case study, and we would rather say that than dress something else up. What we have shipped is adjacent: a DTC Shopify rebuild, a machinery ERP, attendance across three manufacturing plants, and a support copilot for a fintech. Between them they cover checkout and catalogue, multi-site operations, reporting people rely on, and AI running in production with guardrails. On a call we will say plainly which parts of your platform are familiar ground and which we would be learning alongside you.
What does an engagement cost, and how is it structured?
It starts with a 30 minute call. After that we come back with a fixed scope, a timeline and a price, rather than a rate card and an open-ended estimate. If a piece of work is genuinely unknowable up front, such as a migration off a platform we have not seen, we split it out as a short, separately priced piece rather than burying it in a range. The build and the running are priced separately: once you are live, the retainer covers monitoring, fixes to agreed response targets, security updates, improvements and a monthly report.
What happens after launch?
Launch moves you onto the managed retainer, which is the normal end state of an infoloop engagement rather than an upsell. It covers monitoring on the flows that matter, fixes with agreed response targets, security and dependency updates, a steady line of improvements, and a monthly report on uptime, incidents, what we did and what we would do next. In practice it means the people who wrote the ranking logic or the payout job are the ones on the alert when it misbehaves, and there is a rollback path rather than a scramble.
Can you handle traffic spikes from a campaign, a launch or a seasonal peak?
We design for the load you can actually measure, then test above it. That means load testing the read-heavy paths, search, listings and feeds, before a campaign rather than after, caching where the data tolerates it, and keeping the writes that must not fail, such as checkout and payouts, on their own budget. Releases go out behind feature flags with a rollback path, so if a spike exposes a problem the change comes off in minutes. We will also say if the right answer is to change the architecture rather than add servers to it.
How do you use AI agents on a consumer platform without it going wrong?
The same way we put them into production elsewhere: narrow scope, guardrails, monitoring and rollback. An agent gets a defined job, such as drafting a support reply, triaging a moderation report or summarising an order history, with explicit limits on what it can touch and a human confirming anything irreversible like a refund or an account ban. Every action is logged, so a decision can be explained later. We watch answer quality after launch, not just at the demo, and if it drifts the switch is there to turn it off.
Will you work with our existing platform, or do you want to rebuild it?
We work with what is there by default. Rebuilds are expensive, slow, and often recreate the same problems in newer syntax, so we would rather stabilise your current platform, instrument it properly, and replace the parts that genuinely need replacing. Where we do suggest a rebuild, for an unsupported framework or a data model that cannot carry the next feature, we will say which part, why, and what it costs, and sequence it so the platform keeps running throughout. No big-bang switchover weekend.

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Tell us what your platform is losing users to

A 30 minute call, then a fixed scope, timeline and price. If we are not the right team for the work, we will say so on the call rather than three weeks into a discovery phase.

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