Accountable AI for high-volume technical support.

The situation

The client runs a support-heavy operation around a range of specialised products. Support is one of their largest operating costs. A small team handles a very high volume of enquiries each year, covering questions technical enough that a confident but wrong answer can be costly and hard to undo.

The work splits into tiers. Routine questions are quick. Mid-tier questions take longer once the back-and-forth is counted. The hardest cases can absorb significant time and pull the most experienced people away from higher-value work. Years of accumulated support history had left a large but inconsistent knowledge base, and an earlier attempt to point a general-purpose AI at it had fallen short. Without structure and context, the model produced generic or inaccurate answers, which in a technical domain is worse than giving no answer at all.

What we built

We designed and built an AI-augmented support system integrated into the client’s existing ticketing platform, with two capabilities at its core.

Automated triage. As each enquiry arrives, the system checks it for the details needed to resolve it. If something essential is missing, it asks the customer for it before the enquiry reaches a support officer, so the team spends its time on cases that are ready to be worked.

An in-ticket assistant. For each enquiry, the system assembles a short summary, retrieves the relevant material from the client’s verified knowledge base, and drafts a suggested response the officer can review, edit and send. It removes the manual searching and gets the officer to a solution faster.

Underneath sits a retrieval-augmented generation approach on cloud infrastructure, deliberately restricted to the client’s own verified documentation rather than the model’s general knowledge. That is what keeps the guidance accurate and anchored to the client’s own standards.

How we approached it

Two principles shaped the design, and both come from how we think about accountable AI.

Grounded in the client’s own knowledge. The system answers only from verified sources. That is the difference between an assistant that sounds confident and one that is reliably correct, which matters most where a plausible but wrong answer carries real consequences.

Helpful, with a person always in the loop. A human stays in control at every step. If a customer does not supply the detail the system asks for, the enquiry still progresses to a support officer, so no one is left stuck behind the automation. We also built a feedback mechanism so the team can rate each suggestion, giving the client an ongoing, measurable read on how the system performs in production.

The business case

The engagement was built around a business case projecting a substantial annual productivity gain, and a first-year return of roughly 270%, from faster resolution and a lower training burden for new staff. The system was delivered as a short, fixed-scope build and handed over with the performance-tracking dashboard in place, so the client could measure the realised return against that projection over time.

Why this matters if you run a support-heavy operation

If support is one of your largest costs and your products are genuinely technical, an off-the-shelf chatbot tends to make things worse, because a confidently wrong answer erodes trust faster than a slow one. The approach that works is narrower and more disciplined: ground the AI in your own verified knowledge, keep a person in the loop, and measure it honestly once it is live. That is the same discipline we bring to every AI system we build.