AI for Network Operations
Our AI Engine, TuplOS, automates your engineering tasks for customer care and network operations
Request SaaS quoteTelco operators choose Tupl
World’s most advanced operators entrust their
automation initiatives to Tupl
“NOC Automation
is helping to close self-clear tickets, it's the 6th man for our NOC”
“ACCR
enables us to respond to our customers much faster on technical issues”
“Proactive Care is automatically fixing 30,000 customer issues per month, avoiding calls to Customer Care”
“Network Advisor resolved 85% of Congestion and Traffic Imbalance that produced Low Throughput”
Our clients and partners
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Tier-1 Group Operator
LatAm -
Tier-1 Group Operator
Eurasia -
Tier-1 Operator
Japan -
Tier-1 Group Operator
Europe -
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Global recognition for Tupl
An Introduction to Tupl
Frequently Asked Questions
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Is this another CEM solution?
Legacy CEM and SQM solutions put their focus on Visualizing the Customer Experience. Tupl solution builds on top to resolve those problems, in a (closed-loop) automated manner, leveraging Robotics process automation empowered by AI. Tupl solution can add significant and measurable value on top of any existing system, including CEM, for Operations Use Cases (e.g. Customer Care Automation and Network Operation Automation)
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Similar project attempts with complex system integration took us over a year, before we were able to assess any business impact. How long does it take for Tupl?
Thanks to the TuplOS technology, which allows us to leverage existing Big Data and AI technologies, our customers are able to realize benefits from 6 weeks since project commencement. This allows us to offer our solutions also in rental and SaaS mode.
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What does Tupl stand for?
It's short for tuple, multidimensional coordinate system in mathematics, or rows/records in programming. Indicates our passion for using Big Data to solve complex problems in network engineering.
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We are already working with our own Big Data platform. Is your solution compatible?
Tupl is built upon Big Data technologies stack using an open architecture, which allows us to adapt to existing data lakes and to leverage any existing environment and investments.