Insights

Results & Insights

How we put AI into businesses that already run — then route you to services and contact when you are ready to insert it into your stack.

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Insertions shipped
Into live mid-market ops
0%
Avg. cycle-time cut
On targeted workflows
0%
Client Retention
Annualised
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Agents in production
Sitting in existing stacks
Case Studies
CBHS
Health fund · Australia

Problem: Member services still lived across claims, policy, and inbox tools. Straightforward questions bounced between teams, and AI sat in a slide deck — not in the work.

Approach: We wired an agent into the systems they already used: retrieval over approved policy, suggested replies for first-line staff, and a human approval step before anything left the desk.

Outcome: Frontline keeps the same consoles. AI drafts. People decide. Repeat contacts drop.

31% faster first-response on routine member queries.

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 study
Whistleout
Comparison & publishing · Australia

Problem: Plan changes, plan tables, and editorial updates still depended on specialists jumping between CMS, spreadsheets, and partner feeds. Volume went up. The operating model did not.

Approach: We dropped an agentic layer onto the content ops they already ran: ingest partner updates, flag mismatches, draft table and copy diffs, and leave publish with editors.

Outcome: Editors keep the CMS. AI does the grind. Fewer stale plans reach the page.

27% less time from partner update to published table.

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 study
Afurnix
Retail & e-commerce · Europe / APAC

Problem: A growing furniture retailer was answering the same room, stock, and delivery questions across shop, chat, and email — while catalogue and warehouse data already existed in other tools.

Approach: We connected storefront, catalogue, and warehouse data they already had, then added an assistant for shop-floor and inbox: stock, lead times, and room suggestions, with staff confirming the customer-facing reply.

Outcome: Sales and support stay in their existing tools. AI fills the gaps between them.

22% fewer “let me check and come back to you” loops.

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 study