Rambunct

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Overview

$1,000+

Min Project Size

$50 - $99/hr

Avg Hourly Rate

2 - 9

Employees

2021

Year Founded

ABOUT RAMBUNCT

AWS solutions that work for growing businesses

Rambunct is a UK-based AWS Advanced Consulting Partner established in 2022, dedicated to assisting companies with migration, modernization, and efficient operations on AWS. With experience working with over 60 clients worldwide, the team excels in legacy-to-cloud transitions, containerization (EKS/ECS), optimization of cost and performance, automation, and lightweight DevOps engineering tailored for SMBs seeking clarity, stability, and speed without the complexities of enterprise-level solutions. Their core competencies include implementing AWS Connect contact centers, providing end-user computing (EUC) solutions, including digital workplace transformations, and executing generative AI projects utilizing AWS Bedrock and foundation models. Rambunct is recognized for its AWS competencies in SMB, GenAI, and Digital Workplace, with a strong focus on developing AI-driven customer experiences, intelligent automation, and data analytics solutions that produce significant business results.

Languages Spoken

English

HIGHLIGHTS

$1,000+

Min Project Size

$50 - $99/hr

Avg Hourly Rate

2 - 9

Employees

2021

Year Founded

Services

Focus

Industries

Clients

Services Provided

30% Generative AI

15% BI & Big Data Consulting & SI

5% Cloud Consulting & SI

25% Cloud Infrastructure Setup (AWS / GCP / Azure)

15% DevOps Engineering

5% Managed IT Services

5% Cybersecurity Assessments & Hardening

About the Team

OUR STORY

Rambunct is a proud AWS Advanced Tier Services Partner dedicated to delivering tailored cloud solutions that empower small and medium-sized businesses with agility and cost-efficiency. Leveraging AWS's sophisticated automation, redeployment, and configuration methodologies, Rambunct ensures clients gain access to industry-leading processes and expert guidance. Their commitment to thought leadership is evident through insightful content on topics like container orchestration with ECS and EKS, as well as serverless API development with AWS services. By combining deep AWS expertise with a client-focused approach, Rambunct supports businesses in optimizing their cloud infrastructure and accelerating growth in today's dynamic technology landscape.

Portfolio

Real-Time Fleet Insights with Amazon OpenSearch Se

Marti Ileri Teknoloji

**Background**: Marti Ileri Teknoloji collaborated with us to enhance their search and analytics processes using Amazon OpenSearch Service. We provided a secure and scalable architecture that facilitates real-time telemetry insights, sophisticated visualizations, and anomaly detection, all while simplifying operations through a fully managed OpenSearch environment. **The Challenge**: Marti Ileri Teknoloji required an analytics solution that could keep up with the expanding size of their electric vehicle fleet. Their existing systems were unable to handle high-velocity telemetry or deliver real-time dashboards due to scalability and performance limitations. The tools available were insufficient for detecting anomalies and conducting in-depth fleet analysis. Additionally, they needed a fully managed architecture that prioritized security to minimize overhead and enforce stricter controls. **Our Solution**: We created a secure, real-time analytics platform based on Amazon OpenSearch Service. For security and access control, we utilized IAM-based role-based access control (RBAC), AWS Single Sign-On, implemented password policies, and conducted quarterly audits to ensure compliance in access governance. We incorporated advanced analytics through the OpenSearch ML plugin for anomaly detection, a SQL plugin for user-friendly queries, and custom dashboards for time-series and geospatial data. Our scalable architecture features a multi-AZ OpenSearch deployment with VPC endpoints, ensuring secure network boundaries and data ingestion via AWS Lambda and Kinesis. To monitor operations, we implemented CloudWatch metrics and OpenSearch alerting for constant oversight of performance and availability. **The Result**: The modernization with OpenSearch yielded significant improvements in performance, efficiency, and security. Analysis speed increased by 70%, with availability reaching 99.99% through the multi-AZ architecture. Operational overhead was reduced by 40% thanks to the adoption of fully managed services. Real-time anomaly detection is now powered by integrated machine learning models, and security and visibility have been enhanced through RBAC, SSO, and custom dashboards to facilitate strategic decision-making.

Scalable Search & Analytics on OpenSearch

Invia Travel

**Background** Invia Travel Germany GmbH collaborated with us to enhance their search and analytics framework by utilizing Amazon OpenSearch Service. We provided a scalable, cost-effective, and fully managed architecture that supports real-time insights, automates index lifecycle management, and ensures long-term log retention, thereby lowering operational overhead and boosting platform performance. **The Challenge** Invia Travel sought to upgrade an outdated, self-managed Elasticsearch system that was failing to scale effectively. They faced issues related to scalability and maintenance due to escalating data volumes, which complicated cluster management. Additionally, they experienced high storage expenses and a lack of real-time metrics, leading to increased infrastructure costs. There was a pressing need for modernization through a fully managed AWS solution that offered automation and robust storage. **Our Solution** We crafted a powerful OpenSearch architecture tailored for scalability, automation, and cost-effectiveness. This included a tiered setup with Kinesis and Firehose for data ingestion, hot nodes for active data management, and UltraWarm and S3 cold tiers for long-term log storage. We implemented automated lifecycle management using ISM for functions like rollover, replica tuning, merging, snapshots, and cleanup. Performance and cost were optimized through the use of Graviton3 nodes, adaptive shard sizing, adjusted refresh intervals, and compression techniques. We also ensured observability and resilience with CloudWatch for monitoring, dashboards, alerts, multi-AZ deployments, and automated snapshots. **The Result** The modernization of OpenSearch led to significant advancements in performance, automation, and cost efficiency. Long-term storage costs dropped by 90% due to the warm/cold tier approach, while query performance on active data improved by 45%. Indexing throughput increased by 30%, and the full automation of ISM contributed to improved system resilience and compliance through cross-region snapshots and enforceable retention policies.

Scaling ML with Amazon SageMaker MLOps

Relation Therapeutics

**Background** Relation Therapeutics implemented a standardized MLOps framework on Amazon SageMaker to enhance machine learning workflows, boost governance, and speed up scientific research. This new setup offers a scalable, secure, and fully reproducible infrastructure to facilitate their research and model development processes. **The Challenge** Relation Therapeutics aimed to modernize and expand its ML operations to accelerate drug discovery while ensuring robust governance. Their fragmented and informal ML workflows hindered experimentation, while manual tasks in preprocessing, training, and deployment led to inefficiencies. Weak governance compromised traceability, auditability, and compliance. Research teams experienced inconsistent tools and varying access controls. **Our Solution** We suggested and established a cohesive MLOps framework using SageMaker to automate and standardize the entire ML lifecycle. This included end-to-end automation with SageMaker Pipelines for data preparation, training, evaluation, and deployment. We ensured integrated data flow through S3, AWS Glue, and Feature Store for consistent and reusable features. The ML lifecycle was governed with Model Registry, CloudWatch, and CloudTrail for effective version control, monitoring, and auditing. Operations became secure and scalable through IAM-based access control and real-time/batch deployment options. **The Result** The standardized MLOps framework resulted in significant improvements in speed, governance, and collaboration. Model development cycles were three times faster, reducing iteration time from months to weeks. Reproducibility improved by 70% through automated, versioned pipelines. There was a 50% decrease in manual efforts across ML workflows. Performance tracking improved with integrated monitoring and evaluation tools. Compliance strengthened through a centralized model registry and audit logging, while cross-team collaboration benefited from secure, role-based access.

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