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AWS Solution

AI-Ready Data Platform, RAG, and Agentic AI Enablement on AWS

Turn your fragmented data into a production-grade AI foundation

We deploy a production-grade data platform on AWS — Medallion lakehouse, governed pipelines, RAG-powered search, and agentic AI workflows — using Glue, Lake Formation, Bedrock, AgentCore, and OpenSearch so your team ships analytics and AI from the same unified foundation.

Book a Free Assessment

Book a Free Assessment

DELIVERED AT SCALE

30M+

Events processed daily on AWS

4TB+

Data managed per month on AWS

10+ yrs

Data engineering expertise across AWS, GCP & Snowflake

AWS-certified from architecture to delivery

The Problem

Most data teams aren't ready for AI — yet

Building AI applications on top of fragmented, ungoverned data leads to failed projects, duplicated pipelines, and delayed timelines.

Fragmented Data Sources

Multiple disconnected systems — databases, APIs, SaaS tools — with no unified data layer. Every team maintains their own copy.

No Architecture Connecting BI, RAG, and Agentic AI

AWS gives you Bedrock, OpenSearch, and AgentCore — but without a governed data architecture underneath, analytics, RAG, and agentic AI run in separate silos. No shared data layer, no unified governance, no path from dashboards to production AI agents.

Duplicated Pipelines

Engineers rebuild the same ingestion logic for every new project. No reusability, no governance, no shared data contracts between teams.

Why Us

Not just a vendor. A data engineering partner.

Most consultancies hand you a report. We stay until it's in production — We monitor until it's in production and afterwards maintenance support

Production-first mindset

We architect for day-30, not day-1. Every pipeline we build handles real-world scale — 30M+ events/day, 4TB+/month, production-grade from week one.

Full-stack AWS expertise

From Glue and Lake Formation to Bedrock, AgentCore, and OpenSearch — we cover the entire AWS data and AI stack, not just one layer. No handoffs, no knowledge gaps.

Embedded, not outsourced

We work inside your team's tools — Jira, GitHub, Slack. We hire, train, and mentor engineers. We leave your team stronger than we found it.

Measured by outcomes

60–70% ETL performance gains at 10XCRM. 6–8 hours → 30 minutes at Tracium. We define success metrics upfront and are accountable to them.

How We Work

From raw data to AI-ready in three phases

A structured engagement model that builds your foundation correctly.

Phase 1
Weeks 1–2
Assessment & AI Readiness Audit
→Audit your existing stack and map all data sources
→Identify gaps between current state and AI readiness
→Define the AWS architecture blueprint for your workload
Deliverables▾
Architecture gap report
Data source map
AI readiness scorecard
Phase 2
Weeks 3–8
Foundation Build — Medallion Architecture on AWS
→Build Bronze → Silver → Gold data pipeline on S3 + Glue with data quality frameworks and schema enforcement
→Implement Lake Formation governance, cross-source lineage tracking, and access controls
→Deploy the analytics layer — connecting Athena, QuickSight, or your existing BI tools to governed Gold data
Deliverables▾
Production Medallion pipeline with data quality checks
Governed data lake with Lake Formation policies
Analytics layer deployed and validated
Phase 2
Weeks 3–8
Foundation Build — Medallion Architecture on AWS
→Build Bronze → Silver → Gold data pipeline on S3 + Glue with data quality frameworks and schema enforcement
→Implement Lake Formation governance, cross-source lineage tracking, and access controls
→Deploy the analytics layer — connecting Athena, QuickSight, or your existing BI tools to governed Gold data
Deliverables▾
Production Medallion pipeline with data quality checks
Governed data lake with Lake Formation policies
Analytics layer deployed and validated
Phase 3
Weeks 9–14
AI Layer — RAG, Agentic AI & AgentCore
→Deploy RAG pipelines — Bedrock embeddings, OpenSearch vector store, and retrieval optimization connected to your governed Gold data
→Build agentic AI workflows using Amazon Bedrock AgentCore with secure runtime isolation, MCP-compatible tool integration, and Cedar policy controls
→Ship the AI use cases scoped in Phase 1 — intelligent search, autonomous agents, or workflow automation — with evaluation frameworks and guardrails
Deliverables▾
Production RAG pipeline with Bedrock + OpenSearch
Agentic AI workflows deployed via AgentCore
Monitoring, guardrails, and cost controls in place
Phase 3
Weeks 9–14
AI Layer — RAG, Agentic AI & AgentCore
→Deploy RAG pipelines — Bedrock embeddings, OpenSearch vector store, and retrieval optimization connected to your governed Gold data
→Build agentic AI workflows using Amazon Bedrock AgentCore with secure runtime isolation, MCP-compatible tool integration, and Cedar policy controls
→Ship the AI use cases scoped in Phase 1 — intelligent search, autonomous agents, or workflow automation — with evaluation frameworks and guardrails
Deliverables▾
Production RAG pipeline with Bedrock + OpenSearch
Agentic AI workflows deployed via AgentCore
Monitoring, guardrails, and cost controls in place

Well-Architected

Built on the AWS Well-Architected Framework

Every platform we deliver is assessed across all five pillars — so you get architecture that's secure, governed, and built to scale from day one.

Security

Column-level access controls and row-level security via Lake Formation — zero-trust from day one.

Operational Excellence

Automated pipeline monitoring, alerting, and self-healing workflows via CloudWatch and Step Functions.

Reliability

Multi-AZ data lake architecture with automated backups, versioning, and point-in-time recovery on S3.

Performance Efficiency

Athena and Redshift Spectrum for serverless, sub-second analytics — no cluster management required.

Cost Optimisation

S3 intelligent tiering, lifecycle policies, and FinOps dashboards built in from the start — no bill surprises.