Four unrelated back-office processes inside a retail network — meeting room booking, content export, print layout compliance and HR operations in Active Directory — were each too small to justify their own system and collectively expensive. We automated all four as agents on a shared stack, recovering roughly 30% of the staff time they consumed.
Inside a retail network, a set of unrelated back-office processes had quietly grown into a real cost. Meeting rooms were booked by messaging whoever held the calendar. Marketing content was assembled and exported by hand for every campaign. Print layouts went through a manual compliance check where a reviewer looked for the same handful of errors on every file. HR operations — onboarding, offboarding, anniversary notices, internal mailings — were performed one Active Directory click at a time. None of these justified a dedicated system on its own, but together they consumed a meaningful share of the working week and each carried its own quality risk: double-booked rooms, inconsistent content, print errors caught after the run, accounts left active after an employee had left.
A bot that owns room availability end to end and writes bookings into Microsoft Teams, so reservations live in the same calendar employees already use. Conflicts are resolved by the system rather than by whoever replies to the message first.
Automates assembly and export of marketing content that was previously produced by hand for each campaign, keeping structure and formatting consistent across outputs and removing the copy-paste step entirely.
An LLM with vision capability inspects print-ready layouts against the network's requirements, returns a structured JSON verdict per file and rolls the batch up into a summary CSV. Reviewers move from checking every layout to auditing the ones the agent flags. Currently at proof-of-concept stage.
Scripted agents handle hiring and termination workflows, anniversary notifications and internal mailings directly against Active Directory and Microsoft 365 via the Graph API — the operations most exposed to human error, because they are repetitive and consequential.
Across the automated processes, roughly 30% of the staff time previously spent on them was recovered, and the error classes that came with manual handling — booking conflicts, inconsistent content exports, accounts not closed on schedule — largely disappeared, since the agents apply the same rules every time. Building the agents on a shared stack meant each new one cost less than the last: infrastructure, deployment and integration patterns were already in place. Several modules run in production today; the print moderation agent is in proof-of-concept while its accuracy is validated against reviewer judgement.
Tell us what the process looks like today and we will tell you what can be automated — and what should not be.
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