HIVEMIND Success Stories

We have helped customers across industries unlock the full potential of their data with advanced data processing solutions. We have been and are still committed to deliver outstanding results with our clients, and many of them have returned to work with us on multiple projects over the years. We invite you to browse a selection of our portfolio to explore what we have achieved for our clients. 

Don't hesitate to contact us if you're interested in how we can help your business use your data to optimise day-to-day operations to drive growth and success.

State of the art software engineering, consulting & developing

_Explore projects we've done with our clients

near real time anomaly solution

[_Customer: Siemens.]

Our Client had:

Traditional approaches to anomaly detection in grid networks are operator based in a grid opartaing centre. Operators use collected near real time metrics from the grid to asses and antipicate anamolies. The Operator must decide based on experience which mitigating measure to use and lacks the ability to simulate scenarios in real time.

Our Solution:

Hivemind implemented a near real time anamoly solution based on Apache Kafka and Apache Spark, processing 3,6 m data points per sec to determine anamolies in the current and voltage data porints and fingerprint anamalous data sets for simualtion of a downstream predictive systems.

Our Client Needed:

Implement an anamoly detection service with fingerprinting in energy grid networks.


[_Tech Stack.]

Apache Kafka, Apache Spark

Support in design, operation and implementation of the CBIS platform

[_Customer: PAYONE.]

Our Client Needed:

Due to the success of their CBIS platform, management wanted to see new reporting and API's. Therefore, the platform must now be always available and reliable to serve these new requests. Anomolies and problems from the source systems must be identified quickly and data must be up-to-date at all times.

Our Solution:

Hivemind supported PAYONE in the design, operation and implementation of the CBIS platform. Its goal was to unify the different source systems gathered through various mergers into a uniform reporting tool with a central data model. To achieve this we introduced architectures for near real time reporting and streaming as well as cloud services. By using elastic and scalable servies in public clouds we have also implemented cost reduction solutions. The PAYONE team has also been trained on the new architecture and technologies in order to independently maintain the platform.

[_Tech Stack.]

Kafka, Spark, Scala, ZIO, Cats, Akka, AWS (Glue, Athena, S3, ECS, EKS, DMS, RDS, Lambda, API Gateway, Cognito, Secrets Manager), Jenkins, Kafka Connect, Batching and Streaming, ~10M records/day, prospect of >30M records/day, ELM UI

implemented a set of services forming an end-to-end predictions pipeline

[_Customer: ELVAH.]

Our Client Needed:

The main obstacle for EV adoption is range anxiety and the availability of charge points. The charge point market is heterogenous and consist of over 700 providers in Germany alone. The quality of service and the availability greatly differ thus hampering the experience for EV drivers. Elvah imtroduced the scoring platform that gives users the ability to determine the quality and availbaility of charge points from their app.

Our Solution:

Hivemind implemented a scoring solution for Elvah that uses real time and historic session data for all charge points in central europe using machine learning based predictions to allow users to determine the availaibility and quality of charge points at any given time. Hivemind implemented the training models and an MLOps delivery pipeline and a REST endpoint for serving near real time predictions using Apache Spark, Spark ML and microservices running in Kubernetes in AWS

[_Tech Stack.]

Spark, Scala, PostgreSQL, Terraform, Kubernetes, AWS (Glue, S3, SQS, EKS, ECR, Sagemaker), Gitlab Pipelines

State of the art software engineering, consulting & developing

[_Customer: HAYS.]

Our Client Needed:
Hays wanted to create a job board web app to link their clients with potential applicants. The app also had to integrate with their legacy systems

The web app has 2 sides:

1. Talent Applicant side: creating a profile containing their skills; view and track job offer; receive client propositions; schedule interviews and even track time after being hired

2. Client Side: Hays' primary clients. Create job ads, specify the kind of profiles they're looking for, contact applicants and schedule interviews

Our Solution:
We created the entire stack from frontend (Flutter), backend (Scala, microservices, Kafka) and the infrastructure (Kubernetes).

We implemented a matching service between applicants and job offers.

From the beginning, we not only developed the solution but also trained up their developers. Starting from 4 and growing to over 30, integrating them with our team the whole time.

Crucially, we trained them in TDD and setup pipeline merge rules.

This drastically reduced the amount of overtime work they were doing.

State of the art software engineering, consulting & developing
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Why choose Hivemind?


Benefit from the longstanding experience of our senior development team.

We believe in sharing knowledge to empower your own teams.

We always pursue a best-practice approach, striving to create things as simple and efficient as possible.