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Transform

AI and emerging technologies, put into production

For teams who have seen the demos and now want something live. We build agents and copilots on your own data, wrap them in guardrails, and keep them running after launch.

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What this covers

AI and emerging technologies, put into production, end to end.

Agents that finish a task end to end

An agent takes a task from trigger to finished record: reads the systems, decides, acts, logs what it did. Anything that moves money or reaches a customer waits for a person to approve it.

Copilots inside the tools people use

A copilot sits inside the tool your team already has open. It drafts the reply, pulls the account history and suggests the next step. The person stays in charge and can ignore it.

Answers grounded in your own documents

Answers built from your manuals, contracts, policies and past tickets rather than the open internet, with a citation back to the source so anyone can check where the answer came from.

Guardrails and human approval

Rules, confidence thresholds and approval steps around every risky action. Below the threshold the agent asks a person. The limits are written into the scope before the build starts, not added after an incident.

Monitoring, evaluation and rollback

Every run is logged and scored against a set of real cases. When a release drifts, you can roll back to the previous version or switch the agent off without disturbing anything else you run.

Integration with the systems you run

We connect to the CRM, data warehouse, support desk, ERP and internal tools you already use, with least-privilege credentials, so the agent works on live data instead of a stale export.

How it runs

Plan. Build. Run.

01

A 30 minute call

We ask what the work is today: who does it, how often, and what it costs when it goes wrong. If a form or a rule would fix it more cheaply than an agent, we say so on the call.

02

Fixed scope, timeline and price

You get the workflow written down, the systems we would connect, the guardrails and one number, agreed before any work starts. Change requests are scoped and priced separately.

03

Build on your real data

We build against your systems rather than a sandbox, and put it in front of the people who do the job. They test it on real cases and tell us where it is wrong before anything goes live.

04

Go live, then we run it

Release happens behind the guardrails, with monitoring from the first day. After that it is the run retainer: fixes to a response target, improvements, security updates and a monthly report.

Why infoloop

We do not hand over and leave.

  • We stay after launchAn agency hands the files over and leaves. We keep what we build live: monitoring, fixes to an agreed response target, improvements, security updates and a report every month.
  • Built for the second monthA demo only has to work once. Production work needs logging, evaluation, rollback and an off switch, so we build those first and the clever part second.
  • We say when AI is the wrong answerSome work is better fixed with a rule, a form or a tidier process. We would rather scope the smaller thing that works than sell you an agent you do not need.
  • One team builds it and runs itThe people who wrote the thing are the people who keep it running. Nothing is handed to a support desk that has never seen the code or spoken to your team.
  • A record of shipping, not just scoping50+ products shipped, work in 6 countries, a 4.8 average rating and 99.9% uptime on the software we run. We build for businesses where the software has to work on a normal Tuesday.

What you get

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

  • A written scope: the workflow, the systems connected and what the agent may never do.
  • The agent or copilot deployed in your environment and connected to live data.
  • Guardrails: approval steps, confidence thresholds and limits on every risky action.
  • An evaluation set built from real cases, and the scores each release has to clear.
  • Logging and a dashboard showing what ran, what it changed and what it cost.
  • A rollback path, a documented off switch and a handover your own team can read.

Who this is for

Three situations where this is the right call.

Your pilot stalled after the demo

Everyone was impressed and then nothing shipped, usually because nobody owned data access, security or what happens when the model is wrong. We take a prototype the rest of the way and put a name against running it.

A team is buried in repetitive work

Support tickets, order entry, chasing records, rebuilding the same report every week. Volume is rising and hiring is the only plan on the table. We scope the slice an agent can take and leave the judgement calls with people.

Safe matters more than clever

You work somewhere cautious or regulated, and the risk of a confident wrong answer is what is blocking the project. We start from least-privilege access, approvals on anything consequential, logs you can audit and a tested rollback.

Proof

Software we built, and still run.

AI agents and copilots

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Shipping an AI agent to production

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Fintech support copilot

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Questions

What buyers ask us first.

How much does an AI agent or copilot cost?
We price on scope, not on hours. A 30 minute discovery call is usually enough to work out what the agent would do, which systems it touches and where the guardrails go, and you leave with a fixed scope, timeline and price in writing. Engagements split into two parts: a one-off build at a fixed price, and a monthly fee to run it, sized to how much software we keep live. Change requests are scoped and priced separately, so there are no surprise invoices. Indicative starting points for each type of engagement are on our pricing page.
What happens after the agent goes live?
This is the part most projects skip. Once it is live we run it: monitoring in production, fixes within an agreed response target, improvements each month, security updates, and a report showing what the agent handled, what it escalated and what it cost. If quality drifts we roll back to the previous version and investigate. If the workload changes we retune it. The retainer is a single monthly fee sized to what we keep live, and you can pause or stop with notice. We do not hand over and leave.
How do you stop an agent doing something damaging?
Guardrails are designed before the build, not bolted on afterwards. The agent gets least-privilege access, so it can only reach the data and actions its job needs. Anything that moves money, changes a record of consequence or contacts a customer sits behind an approval step or a confidence threshold, and below that threshold the agent asks a person instead of guessing. Every run is logged with what it read and what it changed. There is a tested rollback to the previous version and a documented off switch that stops the agent without disturbing the rest of your systems.
Will it work with the systems we already run?
Usually, yes. We connect to the CRM, data warehouse, help desk, ERP, spreadsheets and internal tools you already use, through their APIs, with credentials scoped to the minimum the agent needs. Where a system has no API we look at exports, database access or the vendor's own integration options, and tell you what that costs you in reliability. Working on live data matters: an agent reasoning over a stale export looks fine in a demo and is wrong in production. If an integration is not sensible, we say so during scoping rather than after you have paid for it.
What happens to our data?
Data handling is agreed in writing as part of the scope, before any build starts. That covers which systems the agent may read, which fields it may write, what leaves your environment, where it is processed and how long anything is kept. Access is least-privilege, and credentials sit in your accounts wherever the platform allows it. Logs record what the agent read and changed, so a decision can be audited after the fact. If your business has rules of its own about where data may go, bring them to the scoping call and we design around them.
How will we know whether it is actually working?
We agree the measure before the build: hours saved on a named task, cost per ticket, time to first response, error rate, whatever the work is judged on today. Then we build an evaluation set from real cases, so each release is scored against examples rather than opinions, and the live numbers go on a dashboard you can open yourself. The monthly report shows what the agent handled, what it escalated to a person and what it got wrong. If the numbers say it is not earning its place, we will tell you and recommend turning it off.

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Tell us the task. We will tell you if AI fits.

Thirty minutes on a call and you leave with a written scope, a timeline and a price. If the job is better done with a rule or a tidier process, we will say that instead.

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