cell towers

events analyzed / second

MB parsed / second

CPU cores

Telecom – processing an impossible data stream.

LakeTide wrote high performance Julia code to parse and analyze MME performance logs in real-time so that valuable insights could be extracted.

Unlocking Performance Data

Telecom operators often have access to performance log-files in proprietary formats for troubleshooting purposes, but are unable to use them directly because of vendor lock-in. LakeTide reverse engineered a number of file formats and unlocked a treasure trove of valuable information on the performance and overall behavior of the network.

Reducing East-West Traffic

Telecom appliances have a limited ability to scale out for the purpose of real-time analytics and monitoring. This places strict requirements on the physical proximity of servers and how much bandwidth they are allowed to consume for the purpose of transmitting performance data. Using the Julia programming language, LakeTide developed code that could processed all the data on a single 8-core server, in real-time, over a single 1Gb/s NIC.

A.I. powered inference

LakeTide trained an A.I. to reproduce performance metrics which under normal circumstances were only available upon purchasing the hardware vendor’s analytics cluster. Moreover, the AI could do inference in real-time and opened up avenues for new business models using the performance data.

LakeTide has shown us an entirely new way of working and extracting value out of data we never even knew we had.

Anders

CxO

Sensors

countries

Predictive Models

milliseconds of latency

Manufacturing – accelerating digitalization, maximizing uptime.

LakeTide used Azure’s stack of big data and machine learning services to capture billions of IoT events and turn them into data driven services.

From Storage to Actionable Insights

By enabling data that was merely stored in the past, it was possible to train deep neural networks that could react to the dynamics of industrial equipment in real-time. Model predictions could then be fed back both to customers and the analytics team for ongoing product improvements.

Geo-distributed IoT Pipeline

LakeTide implemented a geo-distributed multitenant architecture on Azure that allowed smart IoT devices to send and receive data from across the world. The easily managed SaaS applications allowed developers to focus on putting new services into production and deliver new types of value to customers.

Digitalized Customer Experience

Providing customers with an app where they can track the health of their equipment and get notified in advance when to service parts has significantly enhanced the customer experience. By avoiding unscheduled downtime, the new digital experience helps increases customer satisfaction and cuts costs.

We have been collecting data from our devices for a long time! I’m very happy that LakeTide finally helped us use that data in the way we originally imagined.

Dirk

VP Technology

million rows of data

Users enabled

Countries Consolidated

Analytics Platforms

Construction – enabling a data driven organization.

LakeTide used Cloudera Enterprise Data Hub to combine numerous data sources in ways that could be operationalized immediately.

Consolidated Analytics

Data from multiple sources such as social media, customer support, finance, project management, devices, and more are all brought together from every country into one place – the data lake. With a single source for analytical insight and user-friendly interactive dashboards, data driven decision making was truly democratized.

IoT infrastructure

Kafka and Spark Streaming are used to process data such as temperature, power use, noise, humidity, and more, from IoT devices in Bonava’s buildings. In combination with existing operational data, it can be used to provide new innovative services to customers and tenants.

Omnichannel 360° CRM

Customer relationship data is imported into the analytics platform and combined with detailed operational data. This allows Bonava to track the customer journey all the way from a first facebook-session, to a sale, to long-term support – a truly 360° view. By surfacing the data through powerful interactive real-time dashboards, it empowered the organization to make smarter decisions faster.

Sales follow-ups have been lifted to a completely different level.
With this analytics platform, we can focus on taking action!
Thanks for a great and fun cooperation.

Susanne

VP Sales & Customer Service

million images

image classes

GBs of data

Weeks to Completion

FinTech – utilizing AI to analyze images and improve customer services.

LakeTide used GPU accelerated workstations to run machine learning algorithms on a world-class dataset of 20 million+ images and corresponding metadata. The resulting models achieved outstanding performance and catalyzed new forms of product development.

Image Classification

A deep neural network was trained to distinguish between 14000+ extremely similar classes of images. The resulting model reached better-than-human levels of performance at over 43% top-5 accuracy.

Object Detection

A highly custom neural network was designed to detect objects within an image. Existing software could make use of this to ensure that images fulfill important criteria before being sent off for review – significantly decreasing handling time.

Metadata Analysis

A set of additional machine learning models were built to find patterns in company metadata. These models could be used to improve the user experience in existing products by providing intelligent suggestions, detecting anomalies, and much more.

Thank you for a really great engagement! We were impressed by the work you did. Data is the core of our business and we will be capitalizing on the new techniques you’ve introduced. 

Johan

Director of Products & Services

LakeTide AB

Isafjordsgatan 31, 16440 Kista
Stockholm, Sweden
info@laketide.com

Peter Wissinger

CEO
peter.wissinger@laketide.com
+46 708 680 676
LinkedIn

Robert Luciani

CTO
robert.luciani@laketide.com
+46 707 943 269
LinkedIn