the big idea under tech solution

inspiration that powered by AI

Machine Learning, one of the core components of Artificial Intelligence, is the key to the success of our proven method for providing you with the sort of dynamic custom-tailored visual content that sets us apart from our competitors.

Many tech companies already using AI today still struggle with the basics: that being inaccurate content, duplicates, and low image quality.

With this in mind, graphica has worked tirelessly to design and implement our unique technological approach towards working with imagery and minimizing such flaws.

But now before we get into all the nitty-gritty, how about we go back to basics for a moment?

Ok, good.

what goes into building a tech company?

The good news is that we know how to do it and now you're in luck.

Welcome to graphica's Systems Engineering 101.

Full disclosure though: this is not the part where we now teach you how to build a rocket (which sucks we know) but stick with us anyway because chances are you'll still learn a handy thing or two along the way.

before building any application, you first need to understand that the sum of its parts is greater than the whole. system effect

At graphica our key components are simple:

  • The Objects (visuals, users)
  • The Relationships between them (collections, domains)
  • The Metrics we use to quantify these interactions

Metrics is the name given to the set of attributes inherent to each object that allows our machines to distinguish them as separate from one another.

The accuracy of image recognition relies upon the quantity and quality of the chosen metrics.

For example, to be able to distinguish an original image from the millions of copies, we would first need to analyze the date it was uploaded by accessing the external metadata.

we’re processing images with users needs in mind

External metadata is the name of information that's open to the public: essentially The Who, The When and The Where of an object's creation.

Internal metadata is the name of information a computer analyzes when it processes the image as a mathematical object, i.e., in terms of its colour, size, composure, etc.

post-1-metrics

internal and external metadata are the fundamentals of the sorts of data analytics we use here at graphica.

Our job at graphica is to best structure these datasets for machines to recognize and extrapolate from them the most effective and reliable methods of image selection based on a wide range of factors from right across the web.

Moving forward from this starting point, we then develop different algorithms catered toward the effective utilization of the existing data at hand to meet users' needs or supply the system with the new information for a more accurate representation of data in the future.

much like in life itself, it is the ever-increasing complexity of algorithms that continuously makes the system "smarter" and more effective.

Furthermore, this adaptive structure also then allows graphica the freedom to implement any additional algorithms and actualize any further desired functions at a later date, should the need arise.

it’s all about research and hypotheses to experiment with

We here at graphica pride ourselves on our commitment to innovation.

For our customers, this means access to every new and improved feature we release that adds further enhancement to their user experience.

Never ones to rest on our laurels though, and as we live in a rapidly changing interconnected world, graphica is always keen to adjust and widen our scope beyond this initial interface model and focus our efforts toward solving even more complex machine learning problems as well.

We’ve only just started on our journey.

We believe the future is bright. We can solve the designer’s problems by applying artificial intelligence.

Stay tuned to be a part of it.