Telecommunications
Every question about your subscribers, answered on the spot.
Telcos already hold the data to answer almost anything about churn, customer value and revenue. What they do not have is a way to ask it without joining a queue. Conversational agents let finance and marketing interrogate the warehouse directly — with the definitions everyone already agreed on, and the permissions already enforced.
What we hear
- Ad-hoc data requests taking days, by which point the meeting has passed
- Four versions of churn in four different decks
- Customer segmentation refreshed quarterly, in a spreadsheet
- Retention campaigns targeting the wrong customers
Agents built for Telecommunications
Each one says how it works, what it connects to, and where it was built.
Business Performance Agent
Ask the business a question. Get the number, not a ticket.
A Business Performance Agent lets anyone ask the company's numbers a question in plain language — customer count, churn, recurring revenue, average revenue per user — and get a reconciled answer from the governed warehouse. No SQL, no analyst queue, no arguing about whose spreadsheet is right.
Customer Analytics Agent
Which customers are worth the most, and which are about to leave.
A Customer Analytics Agent answers natural-language questions about customer value and segmentation — lifetime value, recency and frequency, who is at risk — from a governed dataset that refreshes daily. Marketing stops guessing which segment to target.
Finance Reporting Agent
The reporting pack, without the week of assembling it.
A Finance Reporting Agent reads directly from the data warehouse and produces the recurring finance reporting that a team would otherwise rebuild by hand every period — with the commentary drafted, not just the figures.
Case Quality Review Agent
Every case reviewed against the rubric, not a sample of ten.
A Case Quality Review Agent scores customer-service responses against your quality rubric — all of them, consistently, rather than the handful a team leader has time to sample. Coaching stops being anecdotal.
Demand Management Agent
Ask the backlog a question instead of reading it.
A Demand Management Agent answers questions about what your product and engineering teams are actually working on, drawing from the tickets, project documentation and decision records that already exist. The institutional memory becomes something you can query.
Product Requirements Agent
Drafts the requirements, and tells you where they are thin.
A Product Requirements Agent generates a product requirements document from the context that already exists across your tools, and scores an existing one against the standard your team agreed — so gaps get caught before engineering finds them.
Sprint Reporting Agent
The status update writes itself, from what actually happened.
A Sprint Reporting Agent produces the recurring engineering status update — what shipped, what slipped, what is blocked — from the ticket tracker, rather than from whatever people remember in the stand-up.
What we have done here
We have built a live customer analytics agent over roughly fifty thousand customers for an ASX-listed telco, and are delivering conversational finance analytics on WisdomAI over Snowflake.
Telecommunications questions
How can AI agents help a telco?
Telcos already hold the data to answer almost anything about churn, customer value and revenue. What they lack is a way to ask it without joining a queue. Conversational analytics agents let finance and marketing interrogate the warehouse directly, using the definitions everyone already agreed on and the permissions already enforced.
How is this different from the dashboards we already have?
A dashboard answers the questions somebody anticipated when they built it. An agent answers the question you actually have. Most organisations end up with both — the dashboard for the numbers you watch every week, the agent for everything else.
Do we need to fix our data before any of this works?
Usually, at least partly — and it is better to hear that early. An agent sitting on unreliable data does not fail loudly, it simply answers wrong, quickly and confidently. We build the governed foundation as well as the conversational layer, because that is where these projects actually succeed or fail.
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