Most contact centres are sitting on the answer
A support operation generates an enormous amount of evidence and reads almost none of it. Every call has a duration, an outcome and a recording. Every ticket has a category, a resolution time and a body of text describing exactly what went wrong. Aggregated, that is a precise account of what your customers struggle with, when they struggle with it, and what it costs you — and in most organisations it is used to produce one number a week that nobody acts on.
What we build
- Operational dashboards — volume, service level, handle time, backlog and resolution, live rather than retrospective, so a bad week is visible on the Tuesday.
- Contact-driver analysis — classifying what people are actually contacting you about and ranking it by volume and cost. Reliably the most uncomfortable and the most useful output, because the top three are usually fixable.
- Forecasting and scheduling — modelling the arrival curve by half-hour, by day and by season, so staffing is planned against expected volume instead of last month's.
- Quality analytics — scoring across a much larger sample of interactions than manual review can reach, surfacing the calls a human analyst should actually listen to.
- Commercial reporting — conversion by campaign, agent and list; cost per resolution; the numbers that decide where next quarter's budget goes.
Where AI genuinely helps
Language models are good at a specific and quite narrow thing: reading large volumes of unstructured text and audio and turning it into structure. That happens to be exactly what a contact centre produces, which is why the fit is real rather than fashionable.
- Classification at scale — sorting a hundred thousand tickets into themes, including the ones your existing categories never had a box for.
- Transcription and summarisation — every call searchable, every conversation summarised into the CRM without an agent typing it up after the customer has hung up.
- Sentiment and escalation signals — finding the interactions that went badly so a human reviews the right ten calls rather than a random ten.
- Draft-and-review — a suggested reply an agent edits and sends. Faster, and the human is still the one accountable for what goes out.
And where it does not. We do not recommend putting a model in front of customers unsupervised on anything with money, safety or a legal consequence attached. A model that is wrong with total confidence is worse than a slow human answer, and the cost lands on your brand rather than on the vendor's. Where automation goes in, the boundary is written into the scope and there is a human check on the far side of it.
The unglamorous part
Most analytics projects fail on data quality rather than on modelling. Disposition codes that agents pick at random, three systems that disagree about who a customer is, timestamps in mixed timezones. The first phase of any engagement is auditing what you actually have and fixing the collection, because a dashboard built on unreliable inputs is worse than no dashboard — it produces confident decisions from noise.
How it works with the rest of the floor
Where we also run your voice or support teams, this line closes the loop: the analysis identifies the leak, the operation fixes it, and the next report shows whether the fix held. Taken on its own, against your in-house team's data, it works just as well — it simply means someone else acts on the findings.
Common questions
Do we need to move our data to you?
Usually not. Wherever possible we work with read access to your existing systems and build reporting on top, so the data stays where it is and your governance is unaffected. Where an export is genuinely necessary, the scope covers what is extracted, where it is held and when it is deleted.
What tools do you build dashboards in?
Whatever you already have and can maintain — Power BI, Looker Studio, Tableau, or the reporting built into your helpdesk if it will stretch far enough. Introducing a new platform to a client who did not need one adds a licence and a dependency, so we only propose it when the existing option genuinely cannot do the job.
Can you analyse call recordings we already have?
Yes, and a back catalogue is often the highest-value place to start, because it gives you a year of contact drivers immediately instead of waiting a quarter to accumulate them. Recordings are transcribed, classified and analysed; the constraints are the consent and retention terms under which they were captured, which we check before anything is processed.
Is this useful if we only have a small team?
Often more useful, because a small team feels every misallocated hour. The engagement is smaller — typically a one-off contact-driver analysis and a simple dashboard rather than an ongoing reporting function — but the finding that three avoidable issues are generating half your tickets is worth as much at ten agents as at two hundred.
How do you handle customer data in AI processing?
The terms are set with you before anything runs: what may be processed, on which platform, and whether it may be retained by that platform. Personal data is minimised or redacted before processing wherever the analysis does not require it. If a constraint in your contracts or your regulator's rules makes a particular approach unavailable, we work within it rather than around it.