Struggling with Fragmented and Unusable Data with no Real-Time Access?

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(A tiny step towards the end of your struggles)

Getting Your Data AI-Ready!

To work better with big data and improve your ability to experiment and optimise high-functioning models, you need access to rich, real-time data. The sort of rich data that comes with well-architected pipelines. We guarantee that it can be done in weeks. That way, you can always access the right data for the right model.

Fall in love with your data again and improve model performance.
We give you what you’ve always wanted:
1

Giving you real-time insight
on large volumes of data. 

2

Automating and simplifying
mundane business processes. 

3

Allowing for the rapid deployment of
high performing algorithms.

Broken Data Equals Broken Models

You’ve hired the best data scientists in the world. They’re remarkable and truly understand AI and all of its potential. But they’re also expensive. And instead of focusing on building powerful predictive models, they spend half their time finding, cleaning and extracting data. It’s infuriating! For them. And for you.

Data acts as a bottleneck to making AI work for companies. The time it takes to fix fragmented, unusable data and create real-time access through pipeline engineering is costly.

Without clean and accessible data, you end up
clock

WASTE

Wasting highly valuable, specialised resources and time.

purple line
obstacles

OBSTACLES

Creating obstacles that prevent experimentation and improvement.

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Algorithms

SETBACKS

Prolonging the inevitable, as Big Data keeps growing (further setting you back).

Ready, set, DEPLOY!
(in 3 Easy Steps)

Our Data Engineering teams innovate and improve your ability to deploy models at scale. They’ve been trained to work closely with your staff and ensure the fluid transfer of necessary skills, knowledge and methods for effective data handling.

We’ve simplified our process into 3 easy steps to achieve Data Engineering success:
1

Fix your broken, siloed data 

2

Build out well-architected, functional data pipelines. 

3
Architect accessible cloud based data lake

We’re here to do the dirty work, so you don’t have to And that’s called freedom.

We know how difficult it is to get your data AI-ready. We know how much time your data science teams spend trying to fix data issues. We know how important it is for them to enhance and improve on the performance of their ML Models. As an AI-Enablement Company we are passionate about building the right expertise:
1

Our team of experts
have deep knowledge in Data Engineering, ML Engineering and Human Experience Design.

2

We have a proven track record
in successfully modernising and enabling AI-functionality for notable global enterprises. 

3
Through world-class best practices,
we are able to make significant transformations in a fraction of the time.
We provide you with the risk-free certainty of a fixed price, fixed timeline solution. During our period of engagement, we develop your team’s ability to become experts at delivering AI solutions.

The Messy Data Problem

For your algorithms to perform effectively, your data needs to be accessible through real-time data pipelines. These pipelines run into big data warehouses that consolidate everything in the cloud. And it’s through the cloud that you have access to unlimited compute, faster network speed and highly resilient infrastructure. 

Introduce messy data into the picture and you’re presented with your biggest stumbling block. Because of the limitations posed by legacy infrastructure, you become restricted to running batch jobs to accommodate your data needs. This alone creates problems within your data. Leaving it disparate, unstructured and messy.

We see this issue everywhere and within every business. It doesn’t matter if
you are a small, medium or even a large corporation, the problem is universal.

So how do you fix this problem?
(Here’s a clue: it rhymes with snipertrailers)

What we recommend is a migration to any of the hyperscalers (you guessed it!). This will allow you access to unlimited, cost-effective compute. You’re able to strictly govern your data and ensure accuracy during the entire data life span. Once messy data is corrected, you’re able to derive real-time data insights and make better-informed business decisions. Your data scientists can then ask the very difficult questions and have far greater accuracy in getting the right answer much faster.

Customers Who Are Making AI Work,
Right Now!

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