Automat-it Rolls Out Data & Analytics Practice for AWS Startups

Automat-it, an AWS Premier Partner and Managed Services Provider that works exclusively with startups, announced the launch of a new Data & Analytics practice. The offering is designed to help startups build the data foundations required to deploy, scale, and optimize Generative AI (GenAI) and machine learning workloads on Amazon Web Services (AWS).

The Problem: Data, Not Models, Is Holding Back AI

Startups are moving fast to deploy GenAI applications, but the biggest obstacle is not the model itself. Fragmented pipelines, poor data quality, and legacy infrastructure increase cloud spend, drain engineering resources, and often keep AI projects from ever reaching production.

What the New Practice Delivers

Automat-its Data & Analytics practice addresses these issues by modernizing a companys data platform, automating data pipelines, and building scalable architectures that support AI and machine learning at scale using AWS-native services. The practice aligns with data mesh principles, domain ownership, and clear service-level agreements, giving startups scalability, trust, and a faster path to value.

A robust data foundation is the difference between an AI experiment and a scalable, production-grade AI product, said Yoav Zuri, CTO at Automat-it. Our new Data & Analytics practice streamlines data preparation and implements scalable lakehouse architectures to transform data from an operational bottleneck into fuel for advanced AI and ML models.

The practice expands Automat-its existing portfolio of AWS-focused services, which already includes DevOps, FinOps, Cloud Security, and GenAI and agentic solutions. Through the new offering, the company helps customers build production-ready data platforms for AI and machine learning workloads, and it supports several specific capabilities:

Core Capabilities

• Data as a Product: DataOps, automated data CI/CD, and data discovery and cataloging to build scalable, consumer-centric data platforms.
• Privacy and Compliance: Automated PII redaction, data masking, and access controls embedded into pipelines to support SOC2, HIPAA, and GDPR-compliant AI workflows.
• Real-Time Streaming: A shift from batch updates to event-driven architectures using Amazon MSK and Kinesis for millisecond-level AI applications and rapid decision-making.
• Business Intelligence: Automated dashboards, including Amazon QuickSight-based tools, to track KPIs, model ROI, and product health.
• Scalable Architectures: Support for RAG, GenAI, and advanced analytics use cases.
• Cost Reduction: Lower infrastructure costs through optimized data architecture and resource utilization.

The Products Behind the Practice

The new practice already includes several named offerings. ETL Modernization brings standardized, automated pipelines that integrate with existing AWS services. The Unified Log Platform is an AWS-native centralized logging solution built for predictable, infrastructure-based pricing and can be deployed in five business days. The Pixel Data Platform automates scalable data pipelines without heavy warehouse costs, converting raw and fragmented data into production-grade intelligence built specifically for RAG pipelines, model optimization, and GenAI workloads on AWS.

The Modern Data Platform Accelerator covers end-to-end ingestion, a Medallion Lakehouse architecture, and automated data quality validation through Deequ. Multimodal Data Lakes for GenAI Training offer specialized architectures with enterprise-grade security, data versioning, and optimized access patterns for text, image, and audio data. Rounding out the practice are Data Platform Proofs of Concept, which allow rapid evaluation of new architectures and tools to validate performance and reduce risk before full production adoption.

Results Automat-it Points To

Automat-it has a track record of driving measurable operational return on investment for data-heavy startups. According to the company, customers using its optimization strategies across data and machine learning architectures have seen model training times reduced by up to 57%, infrastructure costs cut by 40%, and the timeline to deploy production-ready AI solutions shrink from months to a matter of weeks.

We are committed to empowering the startup ecosystem to build, run, and scale securely on AWS, said Ziv Kashtan, CEO at Automat-it. The launch of our D&A Practice means, as startups transition into an AI-first world, they have a trusted partner capable of optimizing their entire journey, from the deepest data pipelines to the highest-level GenAI applications.

What This Means for AWS Startups

For startups already running workloads on AWS, the new practice offers a structured way to close the gap between AI ambition and production reality. Rather than treating data infrastructure as an afterthought, Automat-it is positioning the Data & Analytics practice as the foundation that makes GenAI and machine learning initiatives viable at scale, from initial pipeline design through to full production deployment.

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