Reinvent.
Transform.
Secure.

Investing in high performance Engineering

What we do

The world is transitioning with a speed we have never seen before. Businesses who adapt rightly to the emerging technology manage to remain at the top of the game, by reinventing themselves in the way they operate & deliver value to their customers. Digital transformation plays a key role in shaping up the future of businesses that run in this era. Adfolks place ourselves in the heart of this ever-changing IT landscape. With our deep expertise in technology, our ability to harness transformative progressions and our agile delivery methods, we simply help our clients navigate today’s demanding digital world.

Data Science and Engineering

Focusing on real world, practical applications of data collection and analysis, we look at transforming the data into an enterprise asset to solve complex problems using cutting edge technologies – helping to rapidly implement insights that will drive more informed decision-making.

Marketing Analytics

Everybody’s talking about Customer Data Analytics, yet very few get it right. Getting the right data together & using the right tools to make it accessible for the right people – is an enormous task. Truly passionate data experts combined with a strong team of business intelligence champions can help business achieve what they’ve been dreaming about for a long while.

Digital Infrastructure – Cloud Based & Cloud Native

Modernization that breaks the divide between development and operations by adopting PaaS, micro-services architecture, API Gateway and Management Solutions, containers, Kubernetes, and Serverless technologies enabling businesses to achieve Digital Transformation goals with faster time to production and agility.

Cyber Security Intelligence

In the world of information systems, security logs have gained one influential position. Attempting to fulfill key security needs like real-time monitoring, correlation, processing of security events and replay of historical analysis of log file information, we run in a world where prioritizing the critical is very much a mandate.

ops-brew

Brewing logs
for you.

Collect, Transform and Redirect logs from and to multiple heterogenous sources or destinations Allow ad-hoc access to historical logs.

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Best execution is
NOT optional

Gone are the days where businesses are content with ‘satisfactory’. Progressive & futuristic players look for the very best when embracing emerging technologies. Finding your competitive edge and seeking the right technology that delivers value to your audience should never be optional.

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Our Clients

Case Study

Client
Emaar Properties
Industry
Retail
Technology Used
PostgreSQL, Kubernetes, Kafka, Kong, Golang
Business Challenge:
  • Client’s Property & Complaint Management System, a monolithic application using Oracle DB, incurred heavy license and resource costs and performance issues when it came to BI applications, along with hindrances that came with traditional operations cycle like availability, fault tolerance and resiliency.
Adfolks’ Solution:
  • Re-architectured the application using PostgreSQL.
  • Migrated the application from their on-premise servers to Kubernetes cluster.
  • Containerized the different components in the solution architecture data [Streaming framework, DB, Gateways etc/] on top of Kubernetes.
Results Obtained:
  • Improved the uptime by 99.9%.
  • Brought down resource costs by 25%.
  • Reduced license costs by 80%.
  • Frequent & faster CI/CD releases with zero downtime.
  • Currently looking forward to replicating the same for other key applications.

Client
Majid Al Futtaim Properties
Industry
Retail
Technology Used
Amazon Web Services, Wazuh, Snort, Kafka, Amazon Elastic Search, Amazon Cloud Trail, osquery
Business Challenge:
  • Lack of centralized logging solution to easily monitor and analyse large amounts of data.
  • Underinvested in existing SIEM implementation and having to find budget to increase capacity to meet use cases, incurring heavy licensing costs & operational inefficiency.
  • Unavailability of query-able raw data to replay logs and pinpoint critical logs.
Adfolks’ Solution:
  • Built a Centralized Log Management (CLM) framework that collects logs from different sources, stores & analyses it centrally to redirect the relevant pieces of data to multiple channels, like S3, SIEM etc.
  • Retained RAW logs with replay option using full text queries with Boolean operators to pinpoint critical logs.
  • Provided compliance continuance by using File integrity monitoring, Host intruition detection and Rootkit check.
Results Obtained:
  • Improved Operational Efficiency by 70% by providing real time anomaly detection, security breaches and alert mechanism.
  • Reduced cost overheads by eliminating Splunk license charges & cutting down resource overheads by 30%.
  • Continuous compliance through log analysis, integrity checking, rootkit detection, time-based alerting & active response.

Client
Confidential
Industry
Retail (Mall)
Technology Used
Cisco CMX, Yardi, Qubole, Amazon Kinesis, presto, Apache Spark
Business Challenge:
  • Customer data from multiple points sitting in silos and thus not being used for driving focused business intelligence.
  • Need for collaborating different data points to bring meaningful insights on the overall visitor data, which would later drive decisions towards marketing, operational and rental collaborations.
  • Need for one touchpoint to view data / insights from all datapoints.
Adfolks’ Solution:
  • Engineered real time data streaming and implemented a Big Data Platform to collect data from systems in silos including Wi-Fi & footfall data from Cisco CMX devices, POS data from Yardi Systems and Marketing spend from multiple client systems.
  • Key business use cases were identified across different verticals - sales & marketing, rental ranking operational metrics.
Results Obtained:
  • Zone level comparison mapping against visitor footfall, total engagement, overall sales & total marketing spend.
  • Heatmaps for the overall Mall layouts, per floor, per zones.
  • Marketing spend per square foot and ROI prediction.
  • Rental ranking across Mall layout.
  • In-store targeted marketing & loyalty boosting.
  • Operational metrics mapped against visitor engagement.
  • Shoppers’ time analysis, dwell times & peak hours.
  • Campaign effectiveness prediction and results comparison.

Client
Confidential
Industry
Financial Services
Technology Used
AWS Redshift, AWS Comprehend NLP, Qubole, R, Python, Looker
Business Challenge:
  • Current debt collection requires sizable investment in terms of time, money, manpower and effort. There is no mechanism to predict trends in debt payment due to changes in economy. The number of defaulters account for 18% of the total customers, and the trend has been on the rise since 2016.
Adfolks’ Solution:

3 stage approach.

  • Define Default Probability – logistic regression model on behavioral data, demographic data and aggregated 3rd party data . The overall score defines the odds of default.
  • With the Default probability score an an input to drive the decision of mode of communication with defaulters. Responses and statistically analyzed to arrive at a Advantage Function.
  • Optimize Advantage function help drive the decision to repossession or extend the debt.
Results Obtained:
  • Decreased debt rate and duration.
  • Increased collection efficiency through savings on resources [no. of collectors, time, cost etc].
  • Increased automation within the collection processes.

Client
OSN
Industry
Entertainment Technology
Technology Used
Qubole, PostgreSQL, Spark, Jupyter, Pandas, Deeplearning4j
Business Challenge:
  • Inconsistency in disbursement of credits by call centre agents. Credits given were human decisions based on the conversation with the customers and did not achieve desired result of avoiding churn or achieved the result by giving more credits than required.
Adfolks’ Solution:

Provide intervention strategy based on empirical evidence to allow for consistency in disbursement of credits.

  • Customer and Agent Segmentation based on analysis of subscriber data, call center logs, payment information etc. Patterns are then observed between credits offered and effect on customer behavior.

  • Recommendation engine to get optimal credit value based on regression analysis. Detect inconsistent credit disbursement using autoencoder model.

Results Obtained:
  • Identify the state of mind of customers and provide credits based on customer profile.
  • Real-time credit recommendations on agent chat box based on chat replies.
  • Enable to move towards a system that awards credits with little or no human intervention.

Client
Dubai Airports
Industry
Government
Technology Used
Oracle DB, SQL Server, Azure API Management, Logic Apps, NodeJS, Microsoft Functions
Business Challenge:
  • Dubai Airport, having customer sensitive data needed a secure data channelling mechanism to route their on-prem data to multiple vendors for various business needs.
  • A cost-efficient & protected data channelling was of at most importance for the client who were using on-prem at the time.
Adfolks’ Solution:
  • Introduced Microsoft Azure On-premise Data Gateway for securing the data lying on-prem.
  • Azure API Management & Logic Apps were used to connect to the on-prem data and channel these to different vendors by providing a secure environment. Security measures including Authentication, Data Throttling, Alert mechanism etc. were introduced to ensure the safety of data channelling.
  • Serverless microservice architecture reduced the cost of running application servers.
Results Obtained:
  • Solutioned the business requirement & to implement cost-effective and secure data channelling.
  • Implemented protected data channelling using Azure API Management.
  • Data compliance and Security for sensitive data.
  • Reduced Resource & Cost overheads.

Client
Confidential
Industry
Airline
Technology Used
AWS, Qubole, Spark, Jupyter, Python, talend
Business Challenge:
  • Wastage of perishable food items and the excessive cost involved in the disposal of the expired food items.
Adfolks’ Solution:
  • Create a data pipeline to load data from various sources like flight info, barest info, sales data, route data etc. to Qubole.
  • Our Data Science team formulated a cost function to account for cost of product disposal, storage, insurance, missed opportunity and impact on reputation with a goal to minimize the cost function.
  • Probability density function for each product from historical data based on time, seasonality etc.
Results Obtained:
  • Actionable insights into food trends for various segments and routes.
  • Cost saving on food disposal operations.
  • Ability to take statistical decision under uncertainty, with an impressive degree of accuracy and without bias.