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VynelixAI
Solution

Cloud Data Platform Modernization

Move reporting and data off ageing servers onto a cloud platform that is faster, cheaper to run and ready for AI.

CloudOff the old servers“Will it stay up, stay secure, and not surprise me on the bill?”
Talk to an AI Expert See all problems by industry

Is this you?

If two of these sound familiar, this solution is worth a conversation.

  • “The data warehouse is slow and expensive to grow”
  • “Nightly jobs regularly overrun into the morning”
  • “You cannot start AI projects because the data is stuck on-premise”
What you get

What changes once it is live

  • Faster reports at a lower, usage-based cost
  • Data unblocked for analytics and AI projects
  • Security and privacy controls built in from day one

We migrate legacy warehouses and reporting estates to governed cloud data platforms — rebuilding pipelines, security and cost controls along the way — so analytics and AI run on infrastructure that scales with the business instead of blocking it.

In detail

What’s included — and the impact you should expect

What an engagement actually covers, and what should be different for your business once it is live.

What’s included

  • Assessment of what to move, retire or rebuild
  • Migration of data, reports and pipelines with parallel-run validation
  • Security, privacy and cost controls designed in from day one
  • Decommissioning of legacy systems once the business signs off
Who it’s for

The people who usually sponsor this

If one of these is in the room, this solution is worth the first conversation.

  • 01

    CIOs whose warehouse is slow, expensive and blocking AI projects

  • 02

    CFOs watching infrastructure cost rise faster than usage

  • 03

    Data leaders whose nightly jobs overrun into the morning

How it works

Four steps from the problem you have to the change you want

A path your marketing team can walk a client through — no architecture diagram required.

  1. 01

    Decide what to move, retire or rebuild

    Not everything belongs in the cloud, and not everything should be copied as-is. We make that call with you, system by system.

  2. 02

    Move with a parallel run, not a leap

    Old and new run together until the numbers match and the business signs off. No big-bang weekend.

  3. 03

    Design cost, security and freshness in

    Pay for what you use, encrypt what you store, refresh what the business actually needs — from day one, not as a later project.

  4. 04

    Turn the old lights off

    Once reports, pipelines and users are on the new platform, legacy systems are decommissioned so the saving is real.

A typical engagement

What the first 90 days look like

One real problem, built on real work, measured before we talk about the second use case.

  1. 1Weeks 1–3

    Map and agree the outcome

    An inventory and a sequenced plan: what moves first, what can wait, what can be switched off. Cost and risk on one page.

  2. 2Weeks 4–10

    Build it on real work

    The first subject area live on the new platform, feeding the same reports, with the old job still running as a safety net.

  3. 3Go-live and after

    Run, measure, expand

    The safety net comes down. The next subject area is cheaper because the platform already exists.

By industry

The problem this solves — in each sector

Same building blocks, different pain. Here is how this solution shows up for the industries we work in.

Healthcare & Pharma
The problem
Legacy systems hold data AI projects need.
What changes
A governed cloud data platform with privacy controls built in.
Financial Services
The problem
Warehouse costs rise faster than usage.
What changes
Pay-for-what-you-use analytics with the same controls.
Retail
The problem
Seasonal peaks overwhelm fixed infrastructure.
What changes
Capacity that grows for the season and shrinks afterwards.
See it in action

Watch it work, or read why we build it this way

All demos
Watch demoDemo

Ask your data in plain English — Part 1

A business user types a question; the answer and chart appear in seconds.

Open
Read article4 min read

Data is not agent-ready — and that is why most AI initiatives stall

“Your AI is not the hero. Your data is.” Why fixing data first is the fastest route to AI that works.

Open
See the code

Open-source code for the demos

Your engineers can inspect exactly what they saw in the video.

Open
Works best with

Usually bought together

Most clients combine this with one or two of the following to get the full result.

Data Engineering Platforms

We connect every system you run into one clean, always-fresh source of truth — the plumbing behind every report and every AI.

Data

AI-Ready Cloud Infrastructure

Secure, cost-predictable cloud foundations to run AI and analytics — on AWS, Azure, Google Cloud or your own data centre.

Cloud

Business Intelligence

Dashboards people actually open — with numbers everyone agrees on and a clear owner for each one.

Data
Industries

Where this solution has the most impact

Financial Services

Agentic AI and automation for banks, capital markets, and fintechs operating under strict compliance.

Healthcare & Life Sciences

Clinical and operational AI systems built with patient safety and data privacy at the core.

Retail & E-commerce

Personalization, demand forecasting, and generative content systems for modern retail.

Manufacturing

Predictive maintenance, quality inspection, and agentic automation for the factory floor.

Is Cloud Data Platform Modernization your next step?

Bring one real problem to a 45-minute working session. You leave with the building blocks mapped and a first use case scoped.

Map my problem Talk to an AI Expert