HIVEMIND PORTFOLIO

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 for 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 realised for our clients.]

near real time anomaly solution

[_Customer: Siemens Energy.]

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.

We did:

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 proedictive systems.

[_Tech Stack.]

Apache Spark, Apache Kafka

Support in design, operation and implementation of the CBIS platform

[_Customer: PAYONE.]

Client had:

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. Anomalies and problems from the source systems must be identified quickly and data must be up-to-date at all times.

We did:

Hivemind has supported PAYONE in the design, operation and implementation of the CBIS platform. Its goal is to unify the different source systems they have gathered through various mergers into a uniform reporting tool with a central data model. To achieve this Hivemind has introduced architectures for near real time reporting and streaming as well as cloud services. By using elastic and scalable services in public clouds we have also implemented cost reduction solutions. In addition, the PAYONE team has 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.]

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.

We did:

Hivemind implemented a scoring solution for Elvah that uses real time and historic session data for all charge points in central europe usinhg machine learning based predictions to allow users to dertmine 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 rela time proedictions 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