Media & Telecom
Contact centres and back offices that serve millions of customers every day.
Subscriber behaviour, network telemetry and content signals are already there. We help media and telecom companies turn them into faster service and leaner operations.
- 01
Customer experience enhancement
ML models and AI assistants that personalize customer interactions, predict churn before it happens, and automate resolution, lowering contact center load and allowing better client retention.
- 02
Service innovation
Generative AI solutions for prototyping, testing, and launching new services faster, from personalized content recommendation to AI-powered B2B sales tooling.
- 03
Network performance
Data platforms that monitor network telemetry in real time, detect degradation before it escalates, and optimize resource allocation across infrastructure.
AI in production, story by story.
Pick one to see the result, the client behind it and the full story.
Use Case · Voice Customer Support-5kcalls per month expectedNOS expects 5k fewer calls a month with a conversational voice assistantA conversational virtual voice assistant that reduces call volume and improves customer experienceFull success story 
Use Case · Chat Customer Support50kconversations contained monthlyWOO cuts chat handovers from 55% to 29% and saves around €0.96MAn AI chat assistant that reduces call volume, speeds up resolutions, and improves customer experience for WOOFull success story 
Use Case · Contact Centre Co-Pilot10%handling call time reductionNOS cuts call handling time by 10% with UVA, a knowledge assistantFaster, more consistent answers for customer support operators using a single internal knowledge workspaceFull success story 
Use Case · B2B Sales Assistant+28%more sales meetingsNOS sales teams book 28% more meetings with an AI assistantWe built an AI assistant to help sales teams sell more and betterFull success story 
Use Case · Legal Requests Triage+100requests handled per dayNOS Legal saves around 640 staff hours a week with GenOSA governed workflow to scale legal request handling with full oversightFull success story 
Use Case · Supplier Invoice Processing~20,000Invoices received per monthNOS handles 65% of supplier invoices end to end, with no operatorA governed pipeline to process ~20,000 supplier invoices per month with human review only where it adds valueFull success story 
Use Case · MLOps Platform-75%time-to-market decreased byOne MLOps platform cuts time to market by around 75%A unified MLOps platform to streamline data workflows and accelerate time-to-marketUndisclosed ClientFull success story 
800K+calls per month transcribed, summarised and classifiedNOS turns more than 800K support calls a month into usable dataHundreds of thousands of calls a month, turned into data other teams can useFull success story -5kcalls per month expected50kconversations contained monthly10%handling call time reduction+28%more sales meetings+100requests handled per day~20,000Invoices received per monthUndisclosed Client-75%time-to-market decreased by800K+calls per month transcribed, summarised and classified
AI can be central to transforming the legal sector. It should not be seen as an end in itself, but as a powerful tool to enhance and amplify human work.
Questions
Talk to usDoes this work at telecom scale?
Yes. Our telecom stories handle work such as about 20,000 supplier invoices a month and more than 100 legal requests a day.
What happens after go-live?
The team that builds your solution runs it. We monitor quality and adoption, fix what drifts and improve the solution as your business changes.
Where does our data live?
You decide. We deploy in our cloud, in your own cloud account or on your servers, and your data stays inside that perimeter under your rules.
Do we need an AI strategy before we start?
No. If you already have a use case with an owner, workable data and internal agreement, we can build it. If not, the AI Assessment finds and ranks the opportunities first.
Can you work with our own data and IT teams?
Yes. We adapt to your stack and work side by side with your engineers. We can also plan the move from a DareData team to your own team over time.
Which AI models do you use?
We are not tied to one provider. GenOS works with models such as Claude, GPT, Gemini, Mistral and Llama, and we pick per task for accuracy, cost and where the data may go.
Who owns what we build together?
Ownership is agreed in the contract before work starts. Ask us for our standard terms on the first call.
How is a project priced?
It depends on scope, length and the team it needs. After a first conversation we send a proposal fitted to your case, not a price list.
How do you keep our data secure?
Every solution runs on GenOS, with guardrails, access rules and a log of each answer, action and model call. Your security and risk owners review the design before anything goes live.
How soon do we see results?
The AI Assessment takes two working weeks and ends with a ranked plan. Most of our builds reach production in two to six months, depending on scope and data.
Other industries
Bring us your hardest AI problem.
Large enterprises trust us to carry AI all the way into production. We shape the use case, build it into your systems and run it with the governance your business needs.
- 01Use cases chosen for value, not hype
- 02Systems built into your stack and data
- 03Run, monitored and improved after go-live