MATOFFO

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Overview

$10,000+

Min Project Size

$50 - $99/hr

Avg Hourly Rate

50 - 249

Employees

2017

Year Founded

ABOUT MATOFFO

AWS GenAI Competency PARTNER

Matoffo is a reliable partner in GenAI and cloud innovation, recognized as one of the few AWS GenAI Competency Partners globally. We assist companies in transitioning from experimentation to the secure, large-scale adoption of Generative AI, which enhances operations, increases efficiency, and opens up new revenue streams. Our expertise is backed by AWS's extensive three-month competency evaluation, positioning us among the limited global providers. What We Offer: - GenAI Strategy & Discovery: workshops, ROI modeling, and actionable roadmaps - Secure GenAI Platform Development: enterprise language models (e.g., Claude, Titan, LLaMA, Mistral) customized with proprietary data - MLOps & CI/CD for AI: automated processes, Infrastructure as Code (IaC), and continuous monitoring - AI Assistants & Domain Agents: support tools for sales, HR, finance, IT, and SaaS - AI Training & Enablement: skill development, frameworks, and continuous optimization Reasons Clients Choose Matoffo: - AWS Advanced Tier and GenAI Competency Partner - Proven results, including over 40% increase in engagement, 30% faster resolutions, and 60% faster market entry - Emphasis on security and compliance, including SOC 2 and HIPAA readiness - Multidisciplinary teams consisting of AI/ML engineers, DevOps specialists, and architects - Experience with both startups and large enterprises across finance, security, fintech, and digital transformation Highlighted Case Studies: - Financial Services: AI underwriting and claims processing yielded 78% auto-processed submissions and 80% faster turnaround - Field Service: AI support for 10,000 agents resulted in 15-25% productivity increases and 20-30% improvement in first-time fix rates - Fintech: AI-driven invoice automation achieved 80% quicker processing, quadrupled throughput, and saved $150K - Cloud Security: Implemented GenAI for multi-cloud environments reduced mean time to recovery by 82% and lowered incident rates by 35%, increasing efficiency fivefold At Matoffo, we go beyond just executing AI projects; we revitalize businesses with measurable, secure, and scalable GenAI solutions. Schedule a call to explore further.

Tech Stack

Amazon Application Development FrameworkNodeJSReactVueJSDjangoLaravelReact NativeFlutterNextJSJavaScriptPythonPHP+3 more

Languages Spoken

EnglishUkrainian

HIGHLIGHTS

$10,000+

Min Project Size

$50 - $99/hr

Avg Hourly Rate

50 - 249

Employees

2017

Year Founded

Services

Focus

Industries

Clients

Services Provided+3 Services

13% Generative AI

2% Workflow Automation (Zapier / Make / n8n)

7% AI Strategy & Roadmap Consulting

7% MLOps & Model Deployment Infrastructure

2% IT Staff Augmentation

3% Cloud Consulting & SI

13% Site Reliability Engineering (SRE)

15% Cloud Migration Services

Portfolio

AWS SERVERLESS DATA & ML PIPELINES IMPLEMENTATION

NDA

CLIENT BACKGROUND The client has been developing a data platform aimed at supporting data science applications. Their objective was to collect large volumes of data from various sources, transform and enhance this data for a cohesive representation, and ultimately provide it to machine learning models for both training and prediction. CHALLENGES The client faced significant expenses related to the development, deployment, and, crucially, the operation of the data platform, which encompassed the Data Lake, data ingestion processes, and machine learning pipelines. These pipelines primarily operated on EC2 instances, resulting in elevated operational costs and requiring considerable time for deployment and testing in lower environments. SOLUTION The proposed approach involved breaking down the monolithic ingestion and ML data preparation services into smaller, independent serverless functions. The initial phase includes the Ingestion Layer, where custom-built AWS Lambda functions collect data from various sources and store it in the Data Lake as unstructured data. Subsequently, a series of custom AWS Step Functions retrieve this unstructured data from the Data Lake and convert it into a format suitable for the ML model. TECHNOLOGIES AWS Lambda, AWS Step Functions, Terraform, GitLab CI. BENEFITS The solution significantly lowered both development and deployment costs and time. This was accomplished by implementing infrastructure as code, which facilitates rapid and seamless infrastructure deployment across multiple environments (Development, Staging, Production). Furthermore, throughout the implementation, the team created Lambda and Step functions that initiate data processing based on events, eliminating situations where EC2 instances remained idle and establishing cost-effective Data Lakes. Full version of Case Study - https://matoffo.com/cases/serverless-data-ml-pipelines/

A HIGHLY AVAILABLE MICROSERVICE ARCHITECTURE

BlueGhost

**CLIENT BACKGROUND** BlueGhost is an innovative company that assists businesses in optimizing their operations, thereby saving time and resources while providing actionable event insights. Their wide range of features includes talent bookings, guest lists and comps, event programs, task management and collaboration, as well as CRM and databases. BlueGhost empowers clients to oversee their significant events from a unified source of truth. **CHALLENGES** As BlueGhost grows, it is essential to continually assess the current infrastructure for vulnerabilities. The company recognized an urgent need to improve its existing system, focusing on cost optimization and process automation for deployments. Additionally, it required a contingency plan to quickly revert to the most recent successful deployment to effectively handle any potential failures. **SOLUTION** A new AWS environment was established using a microservice architecture. The Docker image was developed following the latest best practices. Backend services were deployed on AWS Fargate, while the front-end web application was set up as a CDN service with AWS CloudFront. To streamline deployment and release processes, GitLab CI pipelines were created. All sensitive information for the deployment process is securely stored in GitLab CI, and the infrastructure is managed using Terraform, with state files kept in AWS S3. **TECHNOLOGIES** Amazon S3, RDS, Terraform, GitLab CI, Application Load Balancer, Amazon ECS, CloudFront, Docker, ElastiCache, AWS CodeDeploy. For the complete case study, visit: https://matoffo.com/cases/a-highly-available-microservice-architecture/

CLOUD & DEVOPS SERVICES FOR FINTECH PRODUCT

Case Study

CLIENT BACKGROUND Our client is a company that has created a fintech solution aimed at educational institutions. Its goal is to lower transaction costs, enable real-time tracking of financial transactions, and streamline the financial processes within the education sector. CHALLENGES The client recognized the need to automate the deployment of microservices and simplify the release procedures. One key requirement was to transition Docker containers to AWS ECS and to restructure the current infrastructure using Terraform. SOLUTION 1. Amazon Route 53 is utilized for DNS configuration. 2. AWS WAF serves as a web application firewall, safeguarding the Fintech platform from prevalent web threats. 3. Amazon CloudFront acts as a rapid content delivery network (CDN), enhancing the distribution of both static and dynamic web content. 4. Elastic Load Balancing (Application Load Balancer) ensures distribution among the AWS Auto Scaling group, operating the Fintech platform containers across various Availability Zones. 5. Serverless containers hosting the Fintech applications and their associated dependencies operate on the Amazon ECS Cluster, managed by the AWS Fargate service. 6. Amazon ElastiCache for Redis introduces a caching layer for the database. 7. Adopting Amazon Aurora simplifies database management, including features for high availability and multi-master setups. TECHNOLOGIES Terraform, Amazon ECS Cluster, AWS Fargate, Jenkins CI, Amazon Aurora, Amazon CloudFront, Amazon ElastiCache, Amazon Route 53, AWS WAF, Elastic Load Balancing, ESLint, Sonarqube. For the complete Case Study, visit - https://matoffo.com/cases/infrastructure-devops-services-for-fintech-product/

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