AI agents for back-office operations

What gets automated

Triage of shared inboxes and incoming requests

Extracting data from emails, forms and documents

Updating records across CRM, ERP and accounting systems

Checking data against your rules before anything is committed

Preparing work for human approval, with the evidence attached

A full audit trail of what was read, changed and approved

The work between the systems

Most back-office teams already have good software. The ERP, the CRM, the accounting system and the ticketing tool all do their jobs. The hours go into the gaps between them: reading an email and typing it into a system, copying a figure from a PDF, checking one record against another, chasing a missing detail, and updating three places when something changes.

That's the work an AI agent is good at, provided it's built to know its limits.

Two ways agents go wrong

Too rigid. A scripted bot that follows fixed steps works on the cases it was designed for and stops on everything else. The first unusual email sends the work straight back to a person, and nobody saves any time.

Too free. An agent with broad access and no checks will eventually do something confidently wrong in a system of record. One bad bulk update undoes months of saved effort.

We build between the two: agents that handle messy input flexibly, and apply exact rules and human approval wherever being wrong is expensive.

How we design an agent

Scoped permissions. The agent can read and write only what the process needs, system by system.
Rules before writes. Data is validated against your rules, like totals that must match, codes that must exist and limits that must hold, before anything is committed.
Approval where it matters. You decide which actions need a person to approve them. The agent prepares the work and the evidence; a person clicks yes.
An audit trail. Every message read, every field changed and every approval is recorded, so you can always reconstruct why something happened.
Escalation, not guessing. When a case doesn't fit, it goes to a person with everything attached.

Good first processes

Supplier onboarding, invoice and payment queries, credit applications, returns and refunds, order entry, shared-inbox triage, and record updates. The common thread is volume with a pattern: lots of cases that look different on the surface and follow the same logic underneath.

Getting started

Back-office agents are built for your systems, so they start as a custom build, starting with free proof on your own cases and a guaranteed pilot. Where a process fits a standard workflow, it can run on the Nuncily platform instead.

Common questions

What's the difference between an AI agent and RPA?

RPA repeats fixed clicks and breaks when a screen or a document changes. An AI agent reads unstructured input, such as an email or a PDF, works out what's being asked, and responds to what actually arrived. We still use fixed rules wherever the answer must be exact.

How do you stop an agent making mistakes in our systems?

Four ways. It acts only within permissions you define, it validates data against your rules before writing anything, it pauses for human approval wherever you want one, and it records every read and change in an audit trail.

Which processes make good first candidates?

High-volume work that follows a pattern but arrives messily, such as supplier onboarding, invoice and payment queries, credit applications, returns, and shared-inbox triage. Processes where every case needs a fresh judgment call are a poor fit.

Where is our data processed?

The Nuncily platform runs on Amazon Web Services in the London region, encrypted in transit and at rest, and your data is never used to train AI models. For custom builds, hosting is agreed as part of scoping.

How do we start?

Pick one process and send us a sample of real cases for a free run. If the results are worth it, we agree in writing how success will be measured and run a guaranteed pilot with your team approving the agent's work.