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What we do · Data & GenAI

Data & GenAI, one foundation.

We unify your data into a governed lakehouse and ship production GenAI on top — Bedrock & SageMaker, Vertex AI & Gemini, or Azure OpenAI & Fabric — built by an AWS AI Competency partner.

What's included

From scattered data to shipped AI.

  • Lakehouse & analytics — a governed foundation that feeds dashboards and models alike.
  • RAG & GenAI applications — retrieval-grounded assistants and agents on your data.
  • MLOps & governance — reproducible pipelines, evals and guardrails.
  • Vector & feature stores — embeddings and features beside your data.
  • Model choice per cloud — Bedrock, Vertex/Gemini or Azure OpenAI — your call, your account.
GenAI integration

What we deliver.

Secure Data Integration

Connect enterprise data sources to GenAI models using RAG pipelines and vector search — data stays in your account, governed by IAM and KMS.

Automated AI Workflows

End-to-end automation from data ingestion to model inference, built on LangChain, EventBridge, and Lambda — no manual handoffs.

Enterprise Search & Summarisation

Semantic search across your knowledge base using Amazon Kendra and Bedrock — instant, grounded answers from your own documents.

Customized API Integration

GenAI capabilities embedded into your existing applications via managed APIs — Bedrock, SageMaker JumpStart, or open-source LLMs.

Compliance & Governance

All deployments follow AWS Well-Architected, Security Hub best practices, and POPIA requirements — with af-south-1 data residency.

Model Monitoring & Observability

Track model performance, token usage, latency, and cost across every deployed AI workload — with CloudWatch dashboards, drift alerts, and automated retraining triggers.

AI readiness assessment

Is your organisation AI-ready?

We run a structured diagnostic across five dimensions and deliver a clear, actionable report — so you know exactly where you stand before committing to a build.

The 5 dimensions we assess

01

Strategy & Vision

Evaluate GenAI strategy alignment, executive sponsorship, and readiness to embed AI into your business goals.

02

Data Readiness

Assess data quality, accessibility, and governance to determine how well your data can support AI workloads.

03

Infrastructure

Review cloud and on-premise infrastructure for scalability, compute capacity, and integration readiness.

04

People & Skills

Gauge AI literacy across your organisation, identify talent gaps, and assess change-management readiness.

05

Governance & Ethics

Examine policies, risk frameworks, and ethical guidelines to ensure responsible AI deployment.

What you receive

01

Maturity Report

A multi-dimensional diagnostic scoring your GenAI readiness across data architecture, infrastructure, security, talent, and business alignment.

02

Gap Analysis

Identifies architectural bottlenecks, data silo challenges, and quick wins for token spend reduction and model accuracy improvement.

03

AI Roadmap

A phased, prioritised execution blueprint covering infrastructure provisioning, model selection, fine-tuning schedules, and agentic workflows.

04

Benchmarking

Comparative telemetry against industry peers — evaluating compute spend, data maturity, deployment velocity, and LLM implementation efficiency.

05

Expert Consultation

A 1-on-1 technical briefing with a CloudZA AI specialist to stress-test results and unpack legacy integration constraints.

How we work

POC to production, then support.

01

Assess

Data sources, use-cases and a metric that matters.

02

Foundation

Stand up the governed lakehouse and pipelines.

03

Prove

A focused PoC on real data in weeks.

04

Productionize & operate

Harden, ship, and run it with full observability.

Next step

Prove an AI use-case in weeks.

Run the readiness assessment; we scope a PoC on your hyperscaler and take it to production.