Market Problem
AI Market is Growing
at Breakneck Speed
Business executives are frantically implementing and growing AI capabilities to stay competitive
Differential AI
Has fully developed Small Language Model technology that is turnkey and ready to be implemented at scale within organizations
Is entirely capable and ready to build bespoke Large Language Models and Deep Learning for businesses across industries and use-cases
Hover over bars to see CAGR details
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100%
Of C-suite executives have roadmaps to guide their generative AI strategies⁵
Every major C-suite leader surveyed reports having a formal generative AI roadmap in place — signaling AI has moved from experimental to strategic priority at the highest levels of business leadership.
99%
Of C-suite executives have begun identifying generative AI use-cases for revenue growth and cost reduction⁵
Near-universal C-suite engagement in scoping real AI use-cases — both revenue-generating and cost-saving — demonstrates the breadth of enterprise commitment to AI integration.
97%
Of organizations that have invested in AI are reporting positive ROI from implementing AI⁶
An overwhelming majority of companies that have deployed AI are seeing measurable returns, validating AI investment and accelerating adoption across all sectors.
92%
Of business executives expect to boost AI spending within three years⁵
More than nine in ten business leaders plan to actively increase their AI budgets over the next three years, pointing to a sustained and growing market for AI solutions.
[1] Grand View Research, 2023
[2] Grand View Research, 2024
[3] Markets and Markets, 2024
[4] Grand View Research, 2023
[5] McKinsey & Company, 2025
[6] EY, 2024
AI Providers Don't Know
How to Train Efficiently
AI companies are using inefficient processes that unnecessarily cost companies
The conventional method of feeding an AI model everything under the sun is outrageously expensive and unnecessary in building and running highly accurate AI models. AI is a brand-new industry in which very few are educated in optimizing AI training for optimal results.
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Conventional Training Costs Millions
Developers working on AI models with massive quantities of datapoints costs tens or hundreds of millions of dollars, creating a high barrier for new entrants and making it very expensive for organizations to create their own models
The capital requirement alone locks most organizations out of the AI development market — leaving them dependent on expensive third-party cloud providers. Without access to affordable on-premise training, businesses remain at the mercy of external cost structures and recurring fees with no path to model ownership.
69%
of senior leaders are concerned about AI's cost implications
Conventional Training Takes Months
Developers execute training for months. Companies are rushing to implement AI and if they are late to integrate new technology, their competitors will reap the benefit
In a market moving at breakneck speed, months-long training cycles mean companies lose competitive ground before their AI is even deployed. Early movers build institutional knowledge and market advantage that latecomers cannot easily recover. Speed-to-deployment is now a strategic differentiator.
Months
typical time to train a conventional model — while competitors advance
Conventional Models Guzzle Electricity
Holding billions of unnecessary parameters requires many bytes of computations forever after the model is built and trained, requiring massive data centers needlessly utilizing dozens of megawatts of electricity
Persistent energy drain from bloated models isn't just a cost issue — it's a sustainability one. Organizations are increasingly held accountable for their carbon footprint, and massive GPU-driven data centers are incompatible with modern ESG commitments. Smaller, efficient models are the only scalable path forward.
64%
of senior leaders are concerned about AI's negative impact on sustainability and emissions goals
The News Headlines…
Training costs have exponentially increased⁴…
Models need more efficient data use⁴…
To Illustrate the size of electricity needs…
Meta's Llama 3.1 models were trained on over 15 trillion tokens using 16,000 Nvidia H100 GPUs totaling 39.3M GPU hours (1.46M for 8B, 7.0M for 70B, 30.84M for 405B)². This costs Meta hundreds of millions of dollars and enough electricity to power a small country³
"Leaders are waking up to the energy challenges inherent in scaling AI"
Our Differentiation
- 100% On-Premise & Secure
- Trained on your unique context but your data never leaves your firewalls
- You own the model, the data, and the hardware
- Edge compute on even weakest processors
- Cloud compute cost is $0 forever
- Once fine-tuned, the model is a permanent asset with no unknown recurring fees
- 7,000x Smaller Models
- Run directly on small devices, ending with faster inferences
- 10x less energy used, but get the same (or better) accuracy
- The model works when the lights go out or the internet drops
- Ultra-fast models and chatbots
- Faster inferences
- Up to 83% Cheaper & Faster
- Build and fine-tune up to 83% faster than conventional methods
- Up to 83% cheaper training cost versus conventional methods
- 10x faster iterations of future versions
- Even the smallest use and training cases end up as cost effective
- Dedicated Ivy League advisory providing proprietary AI methods unavailable to competitors
- Receive clear, calculable ROI metrics (revenue % and cost savings)
- Get more out of available data
- More interpretable
- Heavyweights in Data Privacy
- Fortune 50 level c-suite experience in AI privacy
- Decades of data governance experience across large-cap enterprises
- Versed in diagnosing and solving data privacy issues, most of which companies aren’t even aware of
- If Interested ?
please contact us for details