Problem: Member services were still split across claims, policy, and inbox tools. Straightforward questions moved between teams, and AI remained in presentations rather than in the work.
Approach: We placed an agent in the systems they already used: retrieval over approved policy, suggested replies for first-line staff, and a human approval step before any response was sent.
Outcome: Frontline staff keep the same consoles. AI prepares drafts. People decide what is sent. Repeat contacts fall.
“We did not rip out our member stack. AI just started sitting in the queue with us — and the easy tickets stopped eating the day.” — Member operations lead
Full case studyProblem: Plan changes, plan tables, and editorial updates still depended on specialists moving between the CMS, spreadsheets, and partner feeds. Volume increased. The operating model did not.
Approach: We added an agentic layer to the content operations they already ran: ingest partner updates, flag mismatches, draft table and copy changes, and leave publishing with editors.
Outcome: Editors keep the CMS. AI handles the repetitive updates. Fewer stale plans reach the page.
“Our editors still own the site. AI just stopped us copying the same plan change into three places by hand.” — Content operations manager
Full case studyProblem: A growing furniture retailer was answering the same room, stock, and delivery questions across the shop, chat, and email, while catalogue and warehouse data already existed in other tools.
Approach: We connected the storefront, catalogue, and warehouse data they already held, then added an assistant for the shop floor and the inbox. It covered stock, lead times, and room suggestions, with staff confirming the customer-facing reply.
Outcome: Sales and support remain in their existing tools. AI connects the information those tools already hold.
“We did not need a new commerce platform. We needed the catalogue and the warehouse to finally talk — and a reply we could send in the same chat.” — Operations manager
Full case studyEach article links to the relevant service lines and ends with CTAs to book a call.
Pillar pieces for this line are coming soon. Request an assessment and we will prioritise what to publish next.
Pillar pieces for this line are coming soon. Request an assessment and we will prioritise what to publish next.
Pillar pieces for this line are coming soon. Request an assessment and we will prioritise what to publish next.
Rule-based automation becomes expensive to maintain as processes change. This article explains where goal-directed agents are a better fit in mid-size operations that already run.
A practical comparison of retrieval-augmented generation and model fine-tuning, including the trade-offs and when each approach is the better fit.
An agentic document pipeline on an accounts-payable process a mid-size team already ran, and how a 73% cost reduction was calculated.
A working demonstration and a production agent are different systems. This is the sequence we use to place agents in a live stack.