Customer service
Call notes written automatically
Each call is transcribed and summarised in your note format: customer intent, issue category, actions taken and next steps, saved to the CRM before the next call starts.
Industries · Telecommunication
AI for telecommunication companies in customer service, network and field
theBlue.ai builds custom AI systems that write call notes, sort tickets and emails, answer questions from your own documentation and watch the data from your equipment. Each system connects to the CRM, ticketing and ERP you already run, such as SAP or your own applications, on-premise, in the cloud or hybrid.
AI in telecommunications: customer service and call documentation
AI in telecommunications evaluates calls, chats and emails. It writes call notes, recognises the customer’s intent and suggests answers while a conversation is running.
In customer service and field operations this supports documentation, ticket routing and the preparation of site visits. Results go to the CRM, the ticket system or the field service portal.
The gain: notes are written during the call, requests reach the right team sooner and repetitive work drops away. Commitments to customers and difficult cases stay with the agents.
Project example · arvato
After every customer call, an agent wrote the summary by hand
At arvato, part of the Bertelsmann group, call centre agents noted the customer’s issue, what was discussed and what happens next after every call. It was slow, the quality varied from agent to agent, and hundreds of hours of call data a day stayed hard to use. theBlue.ai built an MVP of a speech-to-text and summarisation pipeline that writes the note after every call, so agents do not have to do it by hand.
Polish
Custom models for spoken Polish, with its interruptions and colloquialisms
Cloud
Scalable infrastructure for high call volumes
Uniform
One note format across every agent and team
Some of the organisations we have built for
What AI takes over
Nine processes AI takes over in telecommunication
Telecommunication companies handle calls, tickets and equipment data at high volume. AI takes over in customer service, the service desk, the back office, the network and the field.
Customer service
Answers for agents during the call
Agents ask about tariffs, devices and processes in plain language and get the answer from your product and process documentation, with the source, while the customer is still on the line.
Customer service
Chat and voice assistants for customers
Questions about invoices, contracts and faults are answered by chat or voice at any hour, and the conversation passes to an agent with its context when a person is needed.
Service desk
Tickets and emails routed
Incoming tickets and emails are classified by topic and urgency, the customer and contract details are pulled out, and each case goes to the team with the right skills and capacity.
Back office
Orders and contract changes
Orders, contract changes and cancellations that arrive as emails or documents are read and entered into your systems, and only unclear cases go to a person.
Network
Anomalies in equipment data
Operational data from sites and equipment is monitored for anomalies and failing sensors, and maintenance follows the actual condition of each component.
Field service
Technician questions on site
Field technicians ask about devices, installation guides and past cases by text or voice on their phone and get an answer from your technical documentation.
Customer insight
Recurring issues and sentiment
Calls, tickets and social media posts show which issues keep coming back and how customers talk about a new tariff or an outage, as it happens.
Management
Figures on demand
Managers ask for sales, service and planning figures in plain language, by text or voice, and the answer comes straight from SAP, the data warehouse or your own reporting.
How it works
From a customer call to a structured note in the CRM
Source: customer call, 6 minutes
Transcript: automatic Call ended
Transcript: automatic Call ended
Customer: My internet drops out every evening since Monday, and I was charged twice for March.
Agent: I can see the double charge and I’m refunding it now. A technician will check the line on Thursday.
Call note · draft
Customer intent
Report fault, query invoice✓
Issue category
Connection, billing✓
Actions taken
Double charge refunded✓
Next steps
Technician Thursday, time open
Saved to the CRM · 1 field to confirm
Illustrative example with invented data, based on the arvato case study.
Built for telecommunication
What AI in a telecommunication company has to handle
High volumes at peak hours, language as customers actually speak it and many systems that already hold the data.
Spoken language as it is
Varying audio quality, interruptions, colloquial speech and several languages, including languages with thinner out-of-the-box support than English or German.
Built for peak hours
The system scales with the call and ticket volume and keeps working at the busiest hours without slowing down.
Connected to your systems
CRM, ticketing, call recording, SAP or your own applications, on-premise, in the cloud or hybrid.
A person where it matters
The system prepares and routes. Unclear cases, complaints and every decision stay with your agents and teams.
Case studies
Call notes, citizen inquiries and sensor data
Our telecommunication project, and two projects from related fields: inquiries answered across specialised knowledge areas, and anomaly detection in equipment data.
MVP
Automated summaries
NLP
Plus custom models
Telecommunication · Arvato Bertelsmann
Call summaries written by the system, not by the agent
After every customer call, agents typed up a summary by hand. That slowed turnaround and cut into time with customers. Structured summaries are now written automatically.
Read the case study
11 areas
One domain agent each
Auto
Routing, no category picking
Government · Polish institution, under NDA
A multi-agent chatbot across eleven knowledge areas
The old chatbot asked citizens to pick a category before asking, and most did not know which one fitted their question. Specialised agents with retrieval now route each question themselves and match plain language to the technical documentation.
Read the case study
100+
Devices monitored
50+
Sensors on each
Energy & building technology · under NDA
Anomaly detection across more than a hundred devices
Engineers reviewed sensor data from HVAC systems by hand to spot irregularities. AI now watches continuously, detects anomalies, classifies operating modes and flags issues before they escalate.
Read the case studyHow to start
Start with one process and the data behind it
A call centre, a ticket queue or a network of equipment: the work your team does by hand today, and the data it runs on. We check early whether the data carries the use case and where it may be processed.
Describe your process
Tell us where the manual work sits and which data it involves. We come back within one business day with an initial assessment and a proposal for a 30-minute scoping call.
Describe your processStart with the process analysis
Workflows mapped, your data sources and requirements checked, and an architecture proposed with scope, timeline and cost. A standalone engagement with no commitment to proceed.
See the AI Discovery WorkshopFAQ
Questions about AI in telecommunication
In telecommunication, AI takes over work that agents and service teams otherwise do by hand: call notes and answers for agents in customer service, tickets and emails in the service desk, orders and contract changes in the back office, equipment data in the network, technical questions in the field and figures for management. The AI system prepares the result, and people handle the cases that need them.
Yes. For arvato, part of the Bertelsmann group, theBlue.ai built a pipeline that transcribes each customer call and writes the summary in arvato’s own note format: customer intent, issue category, actions taken and next steps. Built as an MVP, it shows that the manual note can be replaced, and the standardised notes make it possible to track recurring issues from actual call data.
The arvato system works on spoken Polish, which has thinner out-of-the-box support than English or German, so theBlue.ai built custom post-processing for audio quality, interruptions and colloquialisms. Other theBlue.ai systems in production work in German and English, and one reads texts in French, Italian and Spanish as well.
Yes. For a Polish technical inspection authority theBlue.ai designed a multi-agent system where people ask in everyday language and specialised agents route the question across 11 knowledge areas. Nobody has to pick a category first, and the answers come from the technical documentation behind them.
Yes. theBlue.ai builds systems into the infrastructure you already run: call recording, CRM, ticketing, SAP, data warehouses or your own applications. At arvato the pipeline connects call recording and CRM and runs on cloud infrastructure that scales with the call volume.
With one process, such as call notes for one service line, and the data behind it. The process analysis has a fixed price from €3k and ends with an architecture proposal that states scope, timeline and cost, including where the data may be processed. The build is priced in milestones, and first working components typically arrive six to eight weeks in.
Tell us which process is costing you the most
Describe the process and we’ll come back within one business day with an initial assessment and a proposal for a 30-minute scoping call.
- Phase 1 is a standalone engagement, with no commitment to proceed further.
- Senior engineers from day one. No account managers, no handoffs.
- We integrate, never replace. Your systems stay exactly as they are.