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Software and AI for B2B SaaS teams

For SaaS teams whose roadmap keeps losing to the support queue and the last release. We take on multi-tenant product work, AI features that go into production with guardrails, and the monthly running that keeps it shipping.

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Where we help

Software and AI for B2B SaaS teams, end to end.

Multi-tenant product work

New features inside the product you already sell. We work in your repository and your conventions, with tenant isolation, roles and plan limits handled in the data model rather than bolted on at the edges.

AI features inside your product

Copilots, assistants and automated workflows that run on customer data. They ship with approvals on anything sensitive, a log of every action taken, and a way to switch a feature off without a deploy.

Support copilots and internal tools

The tickets your team answers the same way every day, drafted or completed automatically, with a person approving anything that touches money. We built one for a fintech support desk and still run it.

Onboarding and activation

Self-serve sign-up, sample data, admin setup and the in-app steps that get a new account to its first real result. Where trials stall, we instrument the path first and change it second.

Integrations, SSO and your public API

The connections enterprise buyers ask about in procurement: single sign-on, webhooks, CRM and billing sync, and a documented API your customers' developers can read without ringing support.

Marketing site and headless CMS

A Webflow or headless CMS site your marketing team edits without an engineer, kept separate from the product so a pricing page change never waits behind a release train.

How it runs

Plan. Build. Run.

01

A 30 minute call

You describe the product, the roadmap and the part of it that keeps slipping. We ask about your stack, your release process and who is on the team. No slides, and nothing to prepare beforehand.

02

Fixed scope, timeline and price

We write down what we will build, when it lands and what it costs, then agree it before any work starts. Change requests are scoped and priced separately, so the number you planned around stays the number.

03

Build in slices, behind flags

Work reaches your staging environment in small pieces rather than one large drop at the end. Every slice sits behind a flag, carries tests, and goes through your review process, so progress is visible weekly.

04

Launch, then run it

We release to a subset of accounts first, watch the numbers, then widen. After that the same team keeps it live: monitoring, fixes, improvements, security updates and a report every month.

Why infoloop

We do not hand over and leave.

  • We stay on after launchThe team that wrote the code runs it. No hand-off to a support desk that has never seen your product, and no knowledge lost in the week after go-live.
  • AI that goes into productionGuardrails, approvals and rollback from the start, not added after a pilot goes wrong. Our fintech support copilot went live on real customer tickets and we still tune it every month.
  • We work in your codebaseYour repository, your conventions, your CI. We do not propose a rewrite because the existing code is unfamiliar, and anything we do replace is scoped and agreed first.
  • One number, not an hourly meterA fixed price for the build and a single monthly fee for running it. No timesheets to interrogate, and no invoice that lands larger than the month you planned for.
  • We run SaaS, not just projectsWe built and still run our own LMS and testing platform: tenants, roles, question banks and AI-assisted grading. We keep the software we run at 99.9% uptime.

What you get

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

  • A written scope, timeline and fixed price agreed before any code is written.
  • A tenant-safe data model with role-based permissions, documented in plain English.
  • Feature flags and a staging environment your own team can test in.
  • Tests and CI checks that run on every pull request we open.
  • Monitoring, error alerting and a rollback path for every release.
  • A monthly report on the metric we agreed to move, and one next step.

Who this is for

Three situations where this is the right call.

The support queue is eating the roadmap

Your team spends its week on refunds, lookups and status questions, and hiring more people only moves the problem along. The answer is usually a copilot on the top three ticket types, with a person approving anything that touches money.

The MVP got you to revenue and now creaks

Permissions were bolted on late, one customer's data model does not fit the next, and releases are something you schedule for a quiet Tuesday. You need parts of it rebuilt without stopping the roadmap for a quarter.

An AI feature is already on the roadmap

It has been announced, or a large customer has asked for it, and nobody on the team has taken a model into production before. You want it built with guardrails and monitoring, and someone on the hook for it afterwards.

Proof

Software we built, and still run.

Fintech support copilot

Read it →

LMS and testing platform

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Questions

What buyers ask us first.

How do you price B2B SaaS work, and what does an engagement look like?
It starts with a 30 minute call. You describe the product and the work, and we come back with a fixed scope, a timeline and a price, usually within one business day. Builds are quoted at a fixed price rather than an hourly rate, and change requests are scoped and priced separately, so the number does not drift while you are planning around it. Most teams then add a monthly we-run retainer: a single fee sized to the software we keep live, which you can pause or stop with notice.
Will you work in our existing codebase, or does everything get rebuilt?
We work inside your repository, your conventions and your CI. A rewrite is a last resort and never the opening proposal. If part of the system genuinely has to be replaced, and with early-stage SaaS that is usually tenant isolation, permissions or a data model that assumed one customer, we scope that part on its own, agree it with you, and do it behind feature flags while the rest of the roadmap keeps moving. Our pull requests go through your review process like anyone else's.
How quickly does something reach production?
Work lands in your staging environment in small slices rather than one drop at the end, so you see something running in the first weeks rather than at the finish. The first release to real accounts usually goes to a subset, gets watched, then widens. We do not quote a timeline before the 30 minute call, because the honest answer depends on your stack, your review process and how much of the work is still discovery. Once we have scoped it, the date is in writing alongside the price.
How do you keep an AI feature safe in a product customers pay for?
The same way we run the fintech support copilot we built and still maintain. The agent only reaches the systems it needs, anything sensitive or financial goes to a person for approval, every action is logged, and the feature can be switched off without a deploy. We start narrow, on the two or three tasks with clear rules, and widen once the numbers hold. Behaviour goes on a dashboard you can see, so the decision to expand rests on measured automation and error rates, not a demo.
What happens after launch?
The team that built it runs it. That means monitoring and alerting, fixes with agreed response targets, improvements shipped every month rather than only when something breaks, security and dependency updates, and a monthly report on the metric we agreed to move with one recommended next step. It is a single monthly fee sized to what we keep live, not an hourly meter, and we keep the software we run at 99.9% uptime. You can pause or stop the retainer with notice.
Who owns the code, and can we bring it in-house later?
You own it. The code sits in your repository under your accounts and the infrastructure runs in your cloud tenancy, so nothing depends on us holding a key. If you hire an in-house team later, the hand-over is a documented codebase, a tenant and permissions model written down, and a walkthrough with the engineers who built it. We would rather keep running it for you, but the retainer is a monthly agreement you can stop with notice, not a lock-in.

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AI & Advanced Tech Solutions

Agents and copilots that do real work in production, with guardrails and rollback.

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Application Maintenance & Support

Monitoring, fixes with response targets, and a monthly report you receive.

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Tell us the part of your roadmap that keeps slipping.

Thirty minutes on your product, your stack and the release you cannot get out. You leave with a fixed scope, a timeline and a price, plus a clear view of what running it would cost each month.

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