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Let's be brutally honest: most AI is absolutely terrible. Not because the technology is flawed, the underlying
mechanisms are actually quite brilliant. The problem is the training data.
When you ask a modern language model about socioeconomic predictions for Bradford, you're not receiving the
carefully considered summation of countless peer-reviewed research papers on the subject. Instead, you're
getting the collective regurgitation of a thousand keyboard warriors from Reddit, armchair economists from
Twitter, and blog posts written by people who've never set foot in Bradford, let alone studied its economy.
That's the fundamental flaw: these models are trained on the internet; a place where any idiot with an opinion
can contribute to the training corpus, and there are a lot of idiots.
The result? AI that's confidently wrong, caught between the garbage training data and a massive list of 'directives' to try and prevent it
telling people that drinking mercury is a good idea for a healthy lifestyle... entertaining, yes, but utterly useless for anything that actually matters.
Developed in 2020, GAIN (GEN's Artificial Intelligence Network) represents a paradigm shift
in how we approach artificial intelligence workflows. Whilst the industry races towards ever faster inference times
and bigger training datasets scraped from increasingly questionable sources, we've taken a different path entirely; one
focused on what truly matters: accuracy.
GAIN doesn't rely on generic models trained on the digital equivalent of pub chat. Instead, it's a sophisticated
network of finely-tuned Large Language Models, each one specifically trained and validated for particular tasks
with proper, vetted data. No Reddit nonsense. No Twitter hot takes. Just properly curated, domain-specific training
that produces results you can actually trust.
GAIN isn't a single monolithic model. It's an intricate ecosystem of specialised Large Language Models,
each one meticulously fine-tuned for specific tasks. These models work collaboratively, creating a
sophisticated network that can tackle the kind of complex, multi-faceted challenges typically reserved
for large corporates with massive budgets.
Think of it as an expert panel rather than a single consultant, each specialist brings their unique
expertise to the table, and together, they achieve what no single model could accomplish alone.
GAIN leverages cascading feedback mechanisms at every stage of processing. Multiple models check and
double-check each other's work, vote on outcomes, and establish quorum on accuracy before proceeding.
This democratic approach to AI ensures that results aren't just fast regurgitations, they're staggeringly precise.
Yes, GAIN isn't the fastest solution available. But speed without accuracy is just expensive noise.
GAIN is designed to be what every other AI solution isn't: dependable, verifiable, and correct.
GAIN's operation is a carefully orchestrated process:
Since its inception in 2020, GAIN has evolved into one of GEN's most prized assets. It's enabled us to
deliver solutions that were previously thought impossible without government-level resources. Through
continuous refinement and the addition of new specialised models, GAIN has grown from an ambitious
experiment into a production ready platform that powers some of our most demanding client projects.
What started as a novel approach to AI orchestration has matured into a robust, battle-tested system
that consistently delivers results that exceed client expectations, not because it's the fastest, but
because it's the most reliable.
At GEN, we've always been at the forefront of innovation. Whilst others follow trends, we create them. Our philosophy has always been simple: do it first, do it right, and do it in ways others haven't conceived yet. We don't always win, and sometimes we waste a ton of money, but this is how innovation works.
Long before "AI" became a buzzword, GEN was pioneering machine learning solutions. Way back in 1990, yes,
you read that correctly, 1990 we developed our first machine learning network. This groundbreaking
system was designed to predict when telephone calls would terminate, and it did so with remarkable accuracy.
Whilst others were still grappling with basic telephony and using erlang calculations, we were already leveraging predictive algorithms
to optimise efficiency and performance. That wasn't just innovation; that was prescience.
That early foray into machine learning set the tone for everything that followed. Through the dot-com
boom, the cloud revolution, and the modern AI renaissance, GEN has consistently been ahead of the curve.
We didn't just adapt to change, we anticipated it.
Our heritage isn't just about being early adopters; it's about being pioneers. When we say we've been
doing AI since before it was fashionable, we're not exaggerating. We have decades of practical experience
that informs every solution we build today.
Our longevity isn't accidental. It's the result of consistently delivering innovative solutions that work. From telecommunications to cloud infrastructure, from cybersecurity to artificial intelligence, we've built our reputation on being the company that does things others can't, or won't attempt.
At gensoftware.dev, we don't just talk about artificial intelligence, we build it. Our development practise is centred around creating practical, production-ready AI and LLM-based systems that solve real-world problems.
Because we've been doing this longer than most companies have existed. Our track record spans decades,
not months. We understand AI not as a trend, but as a tool, and we know how to wield it effectively.
Whether you need a sophisticated multi-model system like GAIN, a straightforward LLM integration, or
something entirely bespoke, we have the expertise, the infrastructure, and the experience to deliver.
We've built AI systems for telecommunications, finance, healthcare, logistics, and countless other sectors.
At GEN, artificial intelligence isn't just another service we offer, it's woven into the very fabric of
who we are. From that first machine learning network in 1990 to GAIN and beyond, we've been pushing the
boundaries of what's possible with intelligent systems. And we're just getting started.